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@ -84,7 +84,7 @@ jobs:
continue-on-error: true
- name: Upload Bandit results
uses: actions/upload-artifact@v4
uses: actions/upload-artifact@v3
if: always()
with:
name: bandit-results

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@ -8,7 +8,7 @@ This example shows how two Claude Code sessions can collaborate on building a Fa
```bash
cd /path/to/bridge
python3 agent_bridge_secure.py /tmp/dev_bridge.db
python3 claude_bridge_secure.py /tmp/dev_bridge.db
```
### Terminal 2: Backend Session (Session A)

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@ -1,269 +0,0 @@
# MCP Multi-Agent Bridge - Ready for GPT-5 Pro Review
**Repository:** https://github.com/dannystocker/mcp-multiagent-bridge
**Branch:** `feat/production-hardening-scripts`
**Status:** ✅ All documentation updated with S² test results and IF.TTT compliance
---
## What's Been Prepared
### 1. Production Hardening Scripts ✅
**Location:** `scripts/production/`
**Files:**
- `README.md` - Complete production deployment guide
- `keepalive-daemon.sh` - Background polling daemon (30s interval)
- `keepalive-client.py` - Heartbeat updater and message checker
- `watchdog-monitor.sh` - External monitoring for silent agents
- `reassign-tasks.py` - Automated task reassignment on failures
- `check-messages.py` - Standalone message checker
- `fs-watcher.sh` - Filesystem watcher for push notifications (<50ms latency)
**Tested with:**
- ✅ 9-agent S² deployment (90 minutes)
- ✅ Multi-machine coordination (cloud + WSL)
- ✅ Automated recovery from worker failures
---
### 2. Complete Documentation Update ✅
**New Documentation:**
#### PRODUCTION.md ⭐ **NEW**
- Complete production deployment guide
- Full test results from November 2025:
- 10-agent stress test (94 seconds, 100% reliability)
- 9-agent S² production hardening (90 minutes)
- Performance metrics with actual numbers:
- 1.7ms average latency (58x better than target)
- 100% message delivery
- Zero race conditions in 482 operations
- IF.TTT citation for production readiness
- Troubleshooting guide
- Known limitations with solutions
**Updated Documentation:**
#### README.md ✅
- **Status:** Changed from "Beta" to "Production-Ready"
- **Statistics:** Updated with real numbers:
- Lines of Code: 6,700 (from ~5,200)
- Documentation: 3,500+ lines across 11 files (from 2,000+ across 7)
- Python Files: 14 (8 core + 6 production scripts)
- **Test Results Section:** Added with actual metrics from stress testing
- **Production Links:** Added links to production hardening scripts
#### RELEASE_NOTES.md ✅
- **New Release:** v1.1.0-production (November 13, 2025)
- **Production Hardening:** Documented all new scripts
- **Test Validation:** Added 10-agent and S² test results
- **Statistics:** Separated v1.0.0-beta and v1.1.0-production stats
- **Roadmap:** Updated with completed features and in-progress items
---
### 3. Real Test Results Documented ✅
**10-Agent Stress Test (November 2025):**
```
Duration: 94 seconds
Agents: 1 coordinator + 9 workers
Operations: 482 total (19 messages + 463 audit logs)
Results:
✅ 1.7ms average latency (58x better than 100ms target)
✅ 100% message delivery (zero failures)
✅ Zero race conditions
✅ Perfect data integrity (SQLite WAL validated)
✅ 463 audit entries (complete accountability)
```
**9-Agent S² Production Hardening (November 2025):**
```
Duration: 90 minutes
Architecture: Multi-machine (cloud + WSL)
Tests: 13 total (8 core + 5 production hardening)
Results:
✅ Idle session recovery: <5 min
✅ Task reassignment: <45s
✅ Keep-alive delivery: 100% over 30 minutes
✅ Watchdog alert: <1 min
✅ Filesystem notifications: <50ms latency
```
---
### 4. IF.TTT Compliance ✅
**Traceable:**
- ✅ Complete audit trail (463 entries in stress test)
- ✅ All code in version control
- ✅ Test results documented with timestamps
- ✅ IF.TTT citations in PRODUCTION.md
**Transparent:**
- ✅ Open source (MIT License)
- ✅ Public repository
- ✅ Full documentation (3,500+ lines)
- ✅ Test results published
- ✅ Known limitations documented
**Trustworthy:**
- ✅ Security validated (482 HMAC operations, zero breaches)
- ✅ Reliability validated (100% delivery, zero corruption)
- ✅ Performance validated (1.7ms latency, 90-min uptime)
- ✅ Automated recovery tested (<5 min reassignment)
**IF.TTT Citation:**
```yaml
citation_id: IF.TTT.2025.002.MCP_BRIDGE_PRODUCTION
claim: "MCP bridge validated for production multi-agent coordination"
validation:
- 10-agent stress test: 482 ops, 1.7ms latency, 100% success
- 9-agent S² test: 90 min, idle recovery, automated reassignment
confidence: high
reproducible: true
```
---
### 5. Statistics Summary ✅
**Code Metrics:**
- Lines of Code: **6,700** (up from ~5,200)
- Python Files: **14** (8 core + 6 production)
- Documentation: **11 files, 3,500+ lines** (up from 7 files, 2,000+ lines)
- Dependencies: **1** (mcp>=1.0.0)
**Test Metrics:**
- Agents Tested: **10** (stress test) + **9** (S² production)
- Total Operations: **482** (all successful)
- Test Duration: **94 seconds** (stress) + **90 minutes** (S²)
- Zero Failures: **0** delivery failures, **0** race conditions, **0** data corruption
**Performance Metrics:**
- Average Latency: **1.7ms** (58x better than 100ms target)
- Message Delivery: **100%** reliability
- Idle Recovery: **<5 minutes**
- Watchdog Detection: **<2 minutes**
- Push Notifications: **<50ms** (428x faster than polling)
---
## Review Checklist for GPT-5 Pro
### Documentation Review
- [ ] **README.md** - Clear, accurate, production-ready status
- [ ] **PRODUCTION.md** - Complete deployment guide with real test results
- [ ] **RELEASE_NOTES.md** - Accurate changelog for v1.1.0-production
- [ ] **scripts/production/README.md** - Clear instructions for production scripts
- [ ] **QUICKSTART.md** - Still accurate for basic setup
- [ ] **SECURITY.md** - Aligned with production hardening features
- [ ] All links working and pointing to correct files
### Technical Accuracy
- [ ] Test results accurately reflect actual testing (verify against `/tmp/stress-test-final-report.md`)
- [ ] Performance numbers are correct (1.7ms latency, 100% delivery, etc.)
- [ ] IF.TTT citations are properly formatted and traceable
- [ ] Known limitations are accurately documented
- [ ] Production recommendations are sound
### Completeness
- [ ] All production scripts documented
- [ ] All test results included
- [ ] Deployment instructions complete
- [ ] Troubleshooting guide comprehensive
- [ ] Statistics up to date
### Production Readiness
- [ ] Security best practices documented
- [ ] Performance characteristics clearly stated
- [ ] Scalability limits documented
- [ ] Monitoring and observability addressed
- [ ] Failure recovery procedures documented
---
## Files Modified
### New Files (10)
1. `PRODUCTION.md` - Production deployment guide
2. `scripts/production/README.md` - Production scripts documentation
3. `scripts/production/keepalive-daemon.sh`
4. `scripts/production/keepalive-client.py`
5. `scripts/production/watchdog-monitor.sh`
6. `scripts/production/reassign-tasks.py`
7. `scripts/production/check-messages.py`
8. `scripts/production/fs-watcher.sh`
9. `GPT5-REVIEW-CHECKLIST.md` - This file
10. (Production test artifacts in infrafabric repo)
### Updated Files (2)
1. `README.md` - Statistics, status, test results
2. `RELEASE_NOTES.md` - v1.1.0-production release
---
## Access Information
**Repository:** https://github.com/dannystocker/mcp-multiagent-bridge
**Branch:** `feat/production-hardening-scripts`
**Pull Request URL:** https://github.com/dannystocker/mcp-multiagent-bridge/pull/new/feat/production-hardening-scripts
**Test Results:**
- Stress test: `/tmp/stress-test-final-report.md`
- S² protocol: `dannystocker/infrafabric/docs/S2-MCP-BRIDGE-TEST-PROTOCOL-V2.md`
---
## Recommended Review Process
1. **Quick Scan (5 min)**
- Read README.md for overview
- Skim PRODUCTION.md for test results
- Check RELEASE_NOTES.md for changelog
2. **Deep Documentation Review (15 min)**
- Verify all statistics match test results
- Check IF.TTT citations for completeness
- Review production deployment instructions
- Validate troubleshooting guide
3. **Technical Review (15 min)**
- Review production scripts for correctness
- Check security best practices
- Validate architecture recommendations
- Verify known limitations
4. **Consistency Check (5 min)**
- Ensure all docs reference same test results
- Verify links between documents
- Check version numbers consistent
- Validate code examples
**Total Time:** ~40 minutes for complete review
---
## Expected Outcomes
After GPT-5 Pro review, we should have:
**Verified accuracy** of all statistics and claims
**Validated completeness** of documentation
**Confirmed production readiness** of deployment guide
**Identified any gaps** in documentation or testing
**Recommendations** for improvements or clarifications
---
**Prepared By:** Claude Sonnet 4.5 (InfraFabric S² Orchestrator)
**Date:** 2025-11-13
**Status:** Ready for Review ✅

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@ -1,473 +0,0 @@
# Production Deployment & Test Results
**Status:** Production-Ready ✅
**Last Tested:** 2025-11-13
**Test Protocol:** S² Multi-Agent Coordination (9 agents, 90 minutes)
---
## Executive Summary
The MCP Multi-Agent Bridge has been **extensively tested and validated** for production multi-agent coordination:
**10-agent stress test** - 94 seconds, 100% reliability
**9-agent S² deployment** - 90 minutes, full production hardening
**Exceptional latency** - 1.7ms average (58x better than target)
**Zero data corruption** - 482 concurrent operations, zero race conditions
**Full security validation** - HMAC auth, rate limiting, audit logging
**IF.TTT compliant** - Traceable, Transparent, Trustworthy framework
---
## Test Results
### 10-Agent Stress Test (November 2025)
**Configuration:**
- 1 Coordinator + 9 Workers
- Multi-conversation architecture (9 separate conversations)
- SQLite WAL mode
- HMAC token authentication
- Rate limiting enabled (10 req/min)
**Performance Metrics:**
| Metric | Target | Actual | Result |
|--------|--------|--------|--------|
| **Message Latency** | <100ms | **1.7ms** | 58x better |
| **Reliability** | 100% | **100%** | ✅ Perfect |
| **Concurrent Agents** | 10 | **10** | ✅ Success |
| **Database Integrity** | OK | **OK** | ✅ Zero corruption |
| **Race Conditions** | 0 | **0** | ✅ WAL mode validated |
| **Audit Trail** | Complete | **463 entries** | ✅ Full accountability |
**Key Statistics:**
- **Total Operations:** 482 (19 messages + 463 audit logs)
- **Latency Range:** 0.8ms - 3.5ms
- **Database Size:** 80 KB (after 482 operations)
- **Zero Failures:** 0 delivery failures, 0 duplicates, 0 data corruption
**Full Report:** See `/tmp/stress-test-final-report.md`
---
### S² Production Hardening Test (November 2025)
**Configuration:**
- 1 Orchestrator + 8 Workers (9 agents total)
- Multi-machine deployment (cloud + local WSL)
- Production hardening: keep-alive daemons, external watchdog, task reassignment
- Test duration: 90 minutes
- Test protocol: S2-MCP-BRIDGE-TEST-PROTOCOL-V2.md
**Advanced Features Tested:**
| Test | Description | Result |
|------|-------------|--------|
| **Test 9** | Idle session recovery | ✅ <5 min reassignment |
| **Test 10** | Cross-machine credential sync | ✅ <65s distribution |
| **Test 11** | Keep-alive daemon reliability | ✅ 100% delivery (30 min) |
| **Test 12** | External watchdog monitoring | ✅ <2 min detection |
| **Test 13** | Filesystem push notifications | ✅ <50ms latency |
**Production Hardening Metrics:**
| Capability | Target | Actual | Result |
|------------|--------|--------|--------|
| **Idle Detection** | <5 min | <3 min | Pass |
| **Task Reassignment** | <60s | <45s | Pass |
| **Keep-Alive Delivery** | 100% | 100% | ✅ Pass |
| **Watchdog Alert** | <2 min | <1 min | Pass |
| **Push Notification** | <100ms | <50ms | Pass |
**Architecture Validated:**
- ✅ 9 agents on separate machines (no shared filesystem)
- ✅ Git-based credential distribution
- ✅ Automated recovery from worker failures
- ✅ Continuous polling with keep-alive daemons
- ✅ External monitoring with watchdog
- ✅ Optional push notifications via filesystem watcher
---
## Production Deployment Guide
### Recommended Architecture
For production multi-agent coordination, we recommend:
```
┌─────────────────────────────────────────┐
│ ORCHESTRATOR AGENT │
│ • Creates N conversations │
│ • Distributes tasks │
│ • Monitors heartbeats │
│ • Runs external watchdog │
└─────────┬───────────────────────────────┘
┌──────┴──────┬─────────┬──────────┐
│ │ │ │
┌──▼───┐ ┌────▼────┐ ┌──▼───┐ ┌──▼───┐
│Worker│ │ Worker │ │Worker│ │Worker│
│ 1 │ │ 2 │ │ 3 │ │ N │
│ │ │ │ │ │ │ │
└──────┘ └─────────┘ └──────┘ └──────┘
│ │ │ │
Keep-alive Keep-alive Keep-alive Keep-alive
daemon daemon daemon daemon
```
### Installation (Production)
1. **Install on all machines:**
```bash
git clone https://github.com/dannystocker/mcp-multiagent-bridge.git
cd mcp-multiagent-bridge
pip install mcp>=1.0.0
```
2. **Configure Claude Code (each machine):**
```json
{
"mcpServers": {
"bridge": {
"command": "python3",
"args": ["/absolute/path/to/agent_bridge_secure.py"]
}
}
}
```
3. **Deploy production scripts:**
```bash
# On workers
scripts/production/keepalive-daemon.sh <conv_id> <token> &
# On orchestrator
scripts/production/watchdog-monitor.sh &
```
4. **Optional: Enable push notifications (Linux only):**
```bash
# Requires inotify-tools
sudo apt-get install -y inotify-tools
scripts/production/fs-watcher.sh <conv_id> <token> &
```
**Full deployment guide:** `scripts/production/README.md`
---
## Performance Characteristics
### Latency
**Measured Performance (10-agent stress test):**
- Average: **1.7ms**
- Min: **0.8ms**
- Max: **3.5ms**
- Variance: **±1.4ms**
**Message Delivery:**
- Polling (30s interval): **15-30s latency**
- Filesystem watcher: **<50ms latency** (428x faster)
### Throughput
**Without Rate Limiting:**
- Single agent: **Hundreds of messages/second**
- 10 concurrent agents: **Limited only by SQLite write serialization**
**With Rate Limiting (default: 10 req/min):**
- Single session: **10 messages/min**
- Multi-agent: **Shared quota across all agents with same token**
**Recommendation:** For multi-agent scenarios, increase to **100 req/min** or use separate tokens per agent.
### Scalability
**Validated Configurations:**
- ✅ **10 agents** - Stress tested (94 seconds)
- ✅ **9 agents** - Production hardened (90 minutes)
- ✅ **482 operations** - Zero race conditions
- ✅ **80 KB database** - Minimal storage overhead
**Projected Scalability:**
- **50-100 agents** - Expected to work well
- **100+ agents** - May need optimization (connection pooling, caching)
---
## Security Validation
### Cryptographic Authentication
**HMAC-SHA256 Token Validation:**
- ✅ All 482 operations authenticated
- ✅ Zero unauthorized access attempts
- ✅ 3-hour token expiration enforced
- ✅ Single-use approval tokens for YOLO mode
### Secret Redaction
**Automatic Secret Detection:**
- ✅ API keys redacted
- ✅ Passwords redacted
- ✅ Tokens redacted
- ✅ Private keys redacted
- ✅ Zero secrets leaked in 350+ messages tested
### Rate Limiting
**Token Bucket Algorithm:**
- ✅ 10 req/min enforced (stress test)
- ✅ Prevented abuse (workers stopped after limit hit)
- ✅ Automatic reset after window expires
- ✅ Per-session tracking validated
### Audit Trail
**Complete Accountability:**
- ✅ 463 audit entries generated (stress test)
- ✅ All operations logged with timestamps
- ✅ Session IDs tracked
- ✅ Action metadata preserved
- ✅ Tamper-evident sequential logging
---
## Database Architecture
### SQLite WAL Mode
**Concurrency Validation:**
- ✅ 10 agents writing simultaneously
- ✅ 435 concurrent read operations
- ✅ Zero write conflicts
- ✅ Zero read anomalies
- ✅ Perfect data integrity
**WAL Mode Benefits:**
- **Concurrent Reads:** Multiple readers while one writer
- **Atomic Writes:** All-or-nothing transactions
- **Crash Recovery:** Automatic rollback on failure
- **Performance:** Faster than traditional rollback journal
**Database Statistics (After 482 operations):**
- Size: **80 KB**
- Conversations: **9**
- Messages: **19**
- Audit entries: **463**
- Integrity check: **✅ OK**
---
## Production Readiness Checklist
### Infrastructure
- [x] SQLite WAL mode enabled
- [x] Database integrity validated
- [x] Concurrent operations tested
- [x] Crash recovery tested
### Security
- [x] HMAC authentication validated
- [x] Secret redaction verified
- [x] Rate limiting enforced
- [x] Audit trail complete
- [x] Token expiration working
### Reliability
- [x] 100% message delivery
- [x] Zero data corruption
- [x] Zero race conditions
- [x] Idle session recovery
- [x] Automated task reassignment
### Monitoring
- [x] External watchdog implemented
- [x] Heartbeat tracking validated
- [x] Audit log analysis ready
- [x] Silent agent detection working
### Performance
- [x] Sub-2ms latency achieved
- [x] 10-agent stress test passed
- [x] 90-minute production test passed
- [x] Keep-alive reliability validated
- [x] Push notifications optional
---
## Known Limitations
### Rate Limiting
⚠️ **Default 10 req/min may be too low for multi-agent scenarios**
**Solution:**
```python
# Increase rate limits in agent_bridge_secure.py
RATE_LIMITS = {
"per_minute": 100, # Increased from 10
"per_hour": 500,
"per_day": 2000
}
```
### Polling-Based Architecture
⚠️ **Workers must poll for new messages (not push-based)**
**Solutions:**
- Use 30-second polling interval (acceptable for most use cases)
- Enable filesystem watcher for <50ms latency (Linux only)
- Keep-alive daemons prevent missed messages
### Multi-Machine Coordination
⚠️ **No shared filesystem - requires git for credential distribution**
**Solution:**
- Git-based credential sync (validated in S² test)
- Automated pull every 60 seconds
- Workers auto-connect when credentials appear
---
## Troubleshooting
### High Latency (>100ms)
**Check:**
1. Polling interval (default: 30s)
2. Network latency (if remote database)
3. Database on network filesystem (use local `/tmp` instead)
**Solution:**
```bash
# Enable filesystem watcher (Linux)
scripts/production/fs-watcher.sh <conv_id> <token> &
# Result: <50ms latency
```
### Rate Limit Errors
**Symptom:** `Rate limit exceeded: 10 req/min exceeded`
**Solutions:**
1. Increase rate limits (see "Known Limitations" above)
2. Use separate tokens per worker
3. Implement batching (send multiple updates in one message)
### Worker Missing Messages
**Symptom:** Worker doesn't see messages from orchestrator
**Check:**
1. Is keep-alive daemon running? `ps aux | grep keepalive-daemon`
2. Is conversation expired? (3-hour TTL)
3. Correct conversation ID and token?
**Solution:**
```bash
# Start keep-alive daemon
scripts/production/keepalive-daemon.sh "$CONV_ID" "$TOKEN" &
```
### Database Locked
**Symptom:** `database is locked` errors
**Check:**
1. WAL mode enabled? `PRAGMA journal_mode;`
2. Database on network filesystem? (not supported)
**Solution:**
```python
# Enable WAL mode (automatic in agent_bridge_secure.py)
conn.execute('PRAGMA journal_mode=WAL')
```
---
## IF.TTT Compliance
### Traceable
✅ **Complete Audit Trail:**
- All 482 operations logged with timestamps
- Session IDs tracked
- Action types recorded
- Metadata preserved
- Sequential logging prevents tampering
✅ **Version Control:**
- All code in git repository
- Test results documented
- Configuration tracked
- Deployment scripts versioned
### Transparent
✅ **Open Source:**
- MIT License
- Public repository
- Full documentation
- Test results published
✅ **Clear Documentation:**
- Security model documented (SECURITY.md)
- YOLO mode risks disclosed (YOLO_MODE.md)
- Production deployment guide
- Test protocols published
### Trustworthy
✅ **Security Validation:**
- HMAC authentication tested (482 operations)
- Secret redaction verified (350+ messages)
- Rate limiting enforced
- Zero security incidents in testing
✅ **Reliability Validation:**
- 100% message delivery (10-agent test)
- Zero data corruption (482 operations)
- Zero race conditions (SQLite WAL validated)
- Automated recovery tested (S² protocol)
✅ **Performance Validation:**
- 1.7ms latency (58x better than target)
- 10-agent concurrency validated
- 90-minute production test passed
- Keep-alive reliability confirmed
---
## Citation
```yaml
citation_id: IF.TTT.2025.002.MCP_BRIDGE_PRODUCTION
source:
type: "production_validation"
project: "MCP Multi-Agent Bridge"
repository: "dannystocker/mcp-multiagent-bridge"
date: "2025-11-13"
test_protocol: "S2-MCP-BRIDGE-TEST-PROTOCOL-V2.md"
claim: "MCP bridge validated for production multi-agent coordination with 100% reliability, sub-2ms latency, and automated recovery from worker failures"
validation:
method: "Dual validation: 10-agent stress test (94s) + 9-agent production hardening (90min)"
evidence:
- "Stress test: 482 operations, 100% success, 1.7ms latency, zero race conditions"
- "S² test: 9 agents, 90 minutes, idle recovery <5min, keep-alive 100% delivery"
- "Security: 482 authenticated operations, zero unauthorized access, complete audit trail"
data_paths:
- "/tmp/stress-test-final-report.md"
- "docs/S2-MCP-BRIDGE-TEST-PROTOCOL-V2.md"
strategic_value:
productivity: "Enables autonomous multi-agent coordination at scale"
reliability: "Automated recovery eliminates manual intervention"
security: "HMAC auth + rate limiting + audit trail provides defense-in-depth"
confidence: "high"
reproducible: true

View file

@ -6,7 +6,7 @@ Production-ready MCP server enabling secure collaboration between two Claude Cod
```
.
├── agent_bridge_secure.py # Main MCP bridge server (secure, production-ready)
├── claude_bridge_secure.py # Main MCP bridge server (secure, production-ready)
├── yolo_mode.py # Command execution extension (use with caution)
├── bridge_cli.py # Management CLI tool
├── test_bridge.py # Test suite
@ -34,7 +34,7 @@ Add to `~/.claude.json`:
"mcpServers": {
"bridge": {
"command": "python3",
"args": ["/absolute/path/to/agent_bridge_secure.py"]
"args": ["/absolute/path/to/claude_bridge_secure.py"]
}
}
}
@ -200,10 +200,10 @@ Before using in production:
cat ~/.claude.json
# 2. Check absolute path
ls -l /path/to/agent_bridge_secure.py
ls -l /path/to/claude_bridge_secure.py
# 3. Test server directly
python3 agent_bridge_secure.py /tmp/test.db
python3 claude_bridge_secure.py /tmp/test.db
# 4. Restart Claude Code
```
@ -227,7 +227,7 @@ python3 bridge_cli.py tokens conv_...
ls -l yolo_mode.py
# 2. Check same directory as bridge
ls -l agent_bridge_secure.py yolo_mode.py
ls -l claude_bridge_secure.py yolo_mode.py
# 3. Test import
python3 -c "from yolo_mode import YOLOMode; print('OK')"

512
README.md
View file

@ -1,204 +1,402 @@
# MCP Multiagent Bridge
Production-ready Python MCP server for secure multi-agent coordination with comprehensive safeguards.
Lightweight Python MCP server for secure multi-agent coordination with configurable rate limiting, auditable actions, and 4-stage YOLO confirmation flow for safe execution.
## Overview
> MCP Multiagent Bridge coordinates multiple LLM agents via the Model Context Protocol (MCP). Designed for experiments and small-scale deployments, it provides battle-tested security safeguards without sacrificing developer experience. Use it to prototype agent orchestration securely — plug in Claude, Codex, GPT, or other backends without rewriting core code.
Enables multiple LLM agents (Claude, Codex, GPT, etc.) to collaborate safely through the Model Context Protocol without sharing workspaces or credentials. Built with security-first architecture and production-grade safeguards.
> ⚠️ **Beta Software**: Suitable for development/testing. See [Security Policy](SECURITY.md) before production use.
**Use cases:**
- Backend agent coordinating with frontend agent on different codebases
- Security review agent validating changes from development agent
- Specialized agents collaborating on complex multi-step workflows
- Any scenario requiring isolated agents to communicate securely
## ⚠️ YOLO Mode Warning
---
This project includes an optional YOLO mode for command execution. This is inherently dangerous and should only be used:
- In isolated development environments
- With explicit user confirmation
- By users who understand the risks
## Key Features
See [YOLO_MODE.md](YOLO_MODE.md) and [SECURITY.md](SECURITY.md) for details.
### 🔒 Security Architecture
## Policy Compliance
**Authentication & Authorization:**
- HMAC-SHA256 session token authentication
- Automatic secret redaction (API keys, passwords, tokens, private keys)
- 3-hour session expiration with automatic cleanup
- SQLite WAL mode for atomic, race-condition-free operations
This project complies with:
- [Anthropic Acceptable Use Policy](https://www.anthropic.com/legal/aup)
- [Anthropic Responsible Scaling Policy](https://www.anthropic.com/responsible-scaling-policy)
**4-Stage YOLO Guard™:**
Command execution (optional) requires multiple confirmation layers:
1. Environment gate - explicit `YOLO_MODE=1` opt-in
2. Interactive typed confirmation phrase
3. One-time validation code (prevents automation)
4. Time-limited approval tokens (5-minute TTL, single-use)
Users are responsible for ensuring appropriate use and maintaining human oversight of all operations.
**Rate Limiting:**
- Token bucket algorithm with configurable windows
- Default: 10 requests/minute, 100/hour, 500/day
- Per-session tracking with automatic reset
- Prevents abuse while allowing legitimate bursts
## Security Features ✅
**Audit Trail:**
- Comprehensive JSONL logging of all operations
- Timestamps, session IDs, actions, results
- Tamper-evident sequential logging
- Supports compliance and forensic analysis
### 🏗️ Production-Ready Architecture
- **Message-only bridge** - No auto-execution, returns proposals only
- **Schema validation** - Strict JSON schemas for all MCP tools
- **Command validation** - Configurable whitelist/blacklist patterns
- **Comprehensive error handling** - Graceful degradation, informative errors
- **Extensible design** - Plugin architecture for future backends
### 📦 Platform Support
**Works with any MCP-compatible LLM:**
- Claude Code, Claude Desktop, Claude API
- OpenAI models (via MCP adapters)
- Anthropic API models
- Custom/future models (not tied to specific backend)
---
- **HMAC Authentication**: Session tokens prevent spoofing
- **Automatic Secret Redaction**: Filters API keys, passwords, private keys
- **Atomic Messaging**: SQLite WAL mode prevents race conditions
- **Audit Trail**: All actions logged with timestamps
- **Token Expiration**: Conversations expire after 3 hours
- **Schema Validation**: Strict JSON schemas for all tools
- **No Auto-Execution**: Bridge returns proposals only - no command execution
- **YOLO Guard**: Multi-stage confirmation for command execution (when enabled)
- **Rate Limiting**: 10 req/min, 100 req/hour, 500 req/day per session
## Installation
```bash
# Clone repository
git clone https://github.com/dannystocker/mcp-multiagent-bridge.git
cd mcp-multiagent-bridge
# Install dependencies
pip install mcp>=1.0.0
pip install mcp
# Run tests
python test_security.py
# Make scripts executable
chmod +x claude_bridge_secure.py bridge_cli.py
# Test the bridge
python3 claude_bridge_secure.py --help
```
Full setup: See [QUICKSTART.md](QUICKSTART.md)
## Quick Start
---
### 1. Configure MCP Server
## Documentation
Add to `~/.claude.json`:
**Getting Started:**
- [QUICKSTART.md](QUICKSTART.md) - 5-minute setup guide
- [EXAMPLE_WORKFLOW.md](EXAMPLE_WORKFLOW.md) - Real-world collaboration scenarios
- [PRODUCTION.md](PRODUCTION.md) - Production deployment & test results ⭐ **NEW**
```json
{
"mcpServers": {
"bridge": {
"command": "python3",
"args": ["/absolute/path/to/claude_bridge_secure.py"],
"env": {}
}
}
}
```
**Production Hardening:**
- [scripts/production/README.md](scripts/production/README.md) - Keep-alive daemons, watchdog, task reassignment ⭐ **NEW**
- [PRODUCTION.md](PRODUCTION.md) - Complete test results with IF.TTT citations
Or use project-scoped config in `.mcp.json` at your project root.
**Security & Compliance:**
- [SECURITY.md](SECURITY.md) - Threat model, responsible disclosure policy
- [YOLO_MODE.md](YOLO_MODE.md) - Command execution safety guide
- Policy compliance: Anthropic AUP, OpenAI Usage Policies
**Contributing:**
- [CONTRIBUTING.md](CONTRIBUTING.md) - Development setup, PR workflow
- [LICENSE](LICENSE) - MIT License
---
## Technical Stack
- **Python 3.11+** - Modern Python with type hints
- **SQLite** - Atomic operations with WAL mode
- **MCP Protocol** - Model Context Protocol integration
- **pytest** - Comprehensive test suite
- **CI/CD** - GitHub Actions (tests, security scanning, linting)
---
## Project Statistics
- **Lines of Code:** ~6,700 (including tests, production scripts + documentation)
- **Test Coverage:** ✅ Core security validated (482 operations, zero failures)
- **Documentation:** 3,500+ lines across 11 markdown files
- **Dependencies:** 1 (mcp>=1.0.0, pinned for reproducibility)
- **License:** MIT
### Production Test Results (November 2025)
**10-Agent Stress Test:**
- ✅ **1.7ms average latency** (58x better than 100ms target)
- ✅ **100% message delivery** (zero failures)
- ✅ **482 concurrent operations** (zero race conditions)
- ✅ **Perfect data integrity** (SQLite WAL validated)
**9-Agent S² Production Hardening:**
- ✅ **90-minute test** (idle recovery, keep-alive, watchdog)
- ✅ **<5 min task reassignment** (automated worker failure recovery)
- ✅ **100% keep-alive delivery** (30-minute validation)
- ✅ **<50ms push notifications** (filesystem watcher, 428x faster than polling)
**Full Report:** See [PRODUCTION.md](PRODUCTION.md)
---
## Development
### 2. Start Session A (Backend Developer)
```bash
# Install dev dependencies
pip install -r requirements.txt
cd ~/projects/backend
# Install pre-commit hooks
pip install pre-commit
pre-commit install
claude-code --prompt "
You are Session A in a multi-agent collaboration.
# Run test suite
pytest
Role: Backend API Developer
# Run security tests
python test_security.py
Instructions:
1. Use create_conversation tool with:
- my_role: 'backend_developer'
- partner_role: 'frontend_developer'
2. Save your conversation_id and token (keep token secret!)
3. Communicate using:
- send_to_partner (to send messages)
- check_messages (poll every 30 seconds)
- update_my_status (keep partner informed)
4. IMPORTANT: Include your token in every tool call for authentication
Task: Design and implement REST API for a todo application.
Coordinate with Session B on API contract before implementing.
Poll for messages regularly with: check_messages
"
```
See [CONTRIBUTING.md](CONTRIBUTING.md) for complete development workflow.
### 3. Start Session B (Frontend Developer)
---
```bash
cd ~/projects/frontend
## Production Status
claude-code --prompt "
You are Session B in a multi-agent collaboration.
**Production-Ready** (Validated November 2025)
Role: Frontend React Developer
**Successfully tested with:**
- ✅ 10-agent stress test (94 seconds, 100% reliability)
- ✅ 9-agent production deployment (90 minutes, full hardening)
- ✅ 1.7ms average latency (58x better than target)
- ✅ Zero data corruption in 482 concurrent operations
- ✅ Automated recovery from worker failures (<5 min)
Instructions:
1. Get conversation_id and your token from Session A
(They should share these securely)
**Recommended for:**
- Production multi-agent coordination
- Development and testing workflows
- Isolated workspaces (recommended)
- Human-supervised operations
- 24/7 autonomous agent systems (with production scripts)
2. Check for messages from Session A:
check_messages with conversation_id and your token
**Production deployment:**
- See [PRODUCTION.md](PRODUCTION.md) for complete deployment guide
- Use [scripts/production/](scripts/production/) for keep-alive, watchdog, and task reassignment
- Follow [SECURITY.md](SECURITY.md) security best practices
3. Reply using send_to_partner
---
4. Poll for new messages every 30 seconds
## Support
Task: Build React frontend for todo application.
Coordinate with Session A on API requirements before implementing.
"
```
- **Issues:** [GitHub Issues](https://github.com/dannystocker/mcp-multiagent-bridge/issues)
- **Discussions:** [GitHub Discussions](https://github.com/dannystocker/mcp-multiagent-bridge/discussions)
- **Security:** See [SECURITY.md](SECURITY.md) for responsible disclosure
## Tool Reference
---
### create_conversation
Initializes a secure conversation and returns tokens.
```json
{
"my_role": "backend_developer",
"partner_role": "frontend_developer"
}
```
**Returns:**
```json
{
"conversation_id": "conv_a1b2c3d4e5f6g7h8",
"session_a_token": "64-char-hex-token",
"session_b_token": "64-char-hex-token",
"expires_at": "2025-10-26T17:00:00Z"
}
```
### send_to_partner
Send authenticated, redacted message to partner.
```json
{
"conversation_id": "conv_...",
"session_id": "a",
"token": "your-session-token",
"message": "Proposed API endpoint: POST /todos",
"action_type": "proposal",
"files_involved": ["api/routes.py"]
}
```
### check_messages
Atomically read and mark messages as read.
```json
{
"conversation_id": "conv_...",
"session_id": "b",
"token": "your-session-token"
}
```
### update_my_status
Heartbeat mechanism to show liveness.
```json
{
"conversation_id": "conv_...",
"session_id": "a",
"token": "your-session-token",
"status": "working"
}
```
Status values: `working`, `waiting`, `blocked`, `complete`
### check_partner_status
See if partner is alive and what they're doing.
```json
{
"conversation_id": "conv_...",
"session_id": "a",
"token": "your-session-token"
}
```
## Management CLI
```bash
# List all conversations
python3 bridge_cli.py list
# Show conversation details and messages
python3 bridge_cli.py show conv_a1b2c3d4e5f6g7h8
# Get tokens (use carefully!)
python3 bridge_cli.py tokens conv_a1b2c3d4e5f6g7h8
# View audit log
python3 bridge_cli.py audit
python3 bridge_cli.py audit conv_a1b2c3d4e5f6g7h8 100
# Clean up expired conversations
python3 bridge_cli.py cleanup
```
## Secret Redaction
The bridge automatically redacts:
- AWS keys (AKIA...)
- Private keys (-----BEGIN...PRIVATE KEY-----)
- Bearer tokens
- API keys
- Passwords
- GitHub tokens (ghp_...)
- OpenAI keys (sk-...)
Redacted content is replaced with placeholders like `AWS_KEY_REDACTED`.
## Security Best Practices
### DO ✅
- Keep session tokens secret
- Use separate workspaces for each session
- Poll for messages regularly (every 30s)
- Update status frequently so partner knows you're alive
- Use `action_type` to clarify message intent
- Review redaction before sending sensitive info
### DON'T ❌
- Share tokens in chat messages
- Commit tokens to version control
- Use expired conversations
- Send unrestricted command execution requests
- Assume messages are end-to-end encrypted (local only)
## Architecture
```
Session A (claude-code) Session B (claude-code)
| |
|--- MCP Tool Calls ---| |
| ↓ |
| Bridge Server |
| (Python + SQLite)
| ↓ |
|--- Authenticated, ---|------|
Redacted Messages
```
### Data Flow
1. Session A calls `create_conversation` → Gets conv_id + token_a + token_b
2. Session A shares conv_id + token_b with Session B
3. Session A calls `send_to_partner` → Message redacted → Stored in DB
4. Session B calls `check_messages` → Retrieves + marks read atomically
5. Session B replies via `send_to_partner`
6. Both sessions update status periodically
### Database Schema
- **conversations**: Conv ID, roles, tokens, expiration
- **messages**: From/to sessions, redacted content, read status
- **session_status**: Current status + heartbeat timestamp
- **audit_log**: All actions for forensics
## Limitations & Safeguards
- **No command execution**: Bridge only passes messages, never executes code
- **3-hour expiration**: Conversations auto-expire
- **50KB message limit**: Prevents token bloat
- **Interactive only**: Human must review all proposed actions
- **No file sharing**: Sessions must use shared workspace or Git
- **Local-only**: No network transport, Unix socket or stdio only
## Testing
```bash
# Basic connectivity test
python3 claude_bridge_secure.py /tmp/test.db &
BRIDGE_PID=$!
# Test tool calls (requires MCP client)
# ... test scenarios ...
kill $BRIDGE_PID
rm /tmp/test.db
```
## Troubleshooting
**"Invalid session token"**
- Check token hasn't expired (3 hours)
- Verify you're using correct token for your session
- Use `bridge_cli.py tokens` to retrieve if lost
**"No MCP servers connected"**
- Verify `~/.claude.json` has correct absolute path
- Restart Claude Code after config changes
- Check MCP server logs: `claude-code --mcp-debug`
**Messages not appearing**
- Confirm both sessions use same conversation_id
- Check token authentication with `bridge_cli.py show`
- Verify partner sent messages (check audit log)
**Redaction too aggressive**
- Review redaction patterns in `SecretRedactor.PATTERNS`
- Consider adding custom patterns if needed
- False positives are safer than leaking secrets
## Use Cases
### 1. API-First Development
- Session A: Backend - designs API, implements endpoints
- Session B: Frontend - consumes API, provides feedback
- **Benefit**: Contract-first design with real-time feedback
### 2. Security Review
- Session A: Feature developer - implements functionality
- Session B: Security auditor - reviews for vulnerabilities
- **Benefit**: Continuous security assessment
### 3. Specialized Expertise
- Session A: Python expert - backend services
- Session B: TypeScript expert - React frontend
- **Benefit**: Each operates in domain of strength
### 4. Parallel Problem-Solving
- Session A: Investigates bug in module X
- Session B: Implements workaround in module Y
- **Benefit**: Non-blocking progress on related tasks
## Advanced Configuration
### Custom Database Location
```bash
python3 claude_bridge_secure.py /path/to/custom.db
```
### Adjust Expiration Time
Edit `create_conversation` method:
```python
expires_at = datetime.utcnow() + timedelta(hours=6) # 6 hours instead of 3
```
### Add Custom Redaction Patterns
Edit `SecretRedactor.PATTERNS`:
```python
PATTERNS = [
# ... existing patterns ...
(r'my_secret_format_[A-Z0-9]{10}', 'CUSTOM_SECRET_REDACTED'),
]
```
## Production Hardening (Future)
Current MVP is designed for local development. For production:
- [ ] Add TLS for network transport
- [ ] Implement rate limiting per session
- [ ] Add message size quotas
- [ ] Enable sandboxed command execution (Docker)
- [ ] Add Redis pub/sub for real-time notifications
- [ ] Implement message encryption at rest
- [ ] Add role-based access control
- [ ] Enable multi-conversation per session
- [ ] Add conversation export/import
- [ ] Implement backup/restore
## License
MIT License - Copyright © 2025 Danny Stocker
MIT - Use responsibly. Not liable for data loss or security issues.
See [LICENSE](LICENSE) for full terms.
## Credits
---
## Acknowledgments
Built with [Claude Code](https://docs.claude.com/claude-code) and [Model Context Protocol](https://modelcontextprotocol.io/).
Inspired by Zen MCP Server's multi-model orchestration concepts.
Built for secure local multi-agent coordination without external dependencies.

View file

@ -1,34 +1,7 @@
# Release Notes - v1.1.0-production
**Release Date:** November 13, 2025
**Status:** Production Release - Validated with Multi-Agent Stress Testing
## 🎉 What's New in v1.1.0
### Production Hardening Scripts ⭐ **NEW**
- **Keep-alive daemons** - Background polling prevents idle session issues
- **External watchdog** - Monitors agent heartbeats, triggers alerts on failures
- **Task reassignment** - Automated recovery from worker failures (<5 min)
- **Filesystem watcher** - Push notifications with <50ms latency (428x faster)
- **Cross-machine sync** - Git-based credential distribution
### Multi-Agent Test Validation ⭐ **NEW**
- ✅ **10-agent stress test** - 94 seconds, 100% reliability, 1.7ms latency
- ✅ **9-agent S² deployment** - 90 minutes, full production hardening
- ✅ **482 concurrent operations** - Zero race conditions, perfect data integrity
- ✅ **Automated recovery** - Worker failure detection + task reassignment validated
### Documentation Enhancements
- **PRODUCTION.md** - Complete production deployment guide with test results
- **scripts/production/README.md** - Production script documentation
- **IF.TTT citations** - Full Traceable, Transparent, Trustworthy compliance
---
# Release Notes - v1.0.0-beta
**Release Date:** October 27, 2025
**Status:** Beta Release - Initial Public Release
**Status:** Beta Release - Production-Ready for Development/Testing Environments
---
@ -71,7 +44,7 @@ Claude Code Bridge is a secure, production-lean MCP server that enables two Clau
## 📦 What's Included
### Core Components
- **`agent_bridge_secure.py`** - Main MCP server with rate limiting
- **`claude_bridge_secure.py`** - Main MCP server with rate limiting
- **`yolo_guard.py`** - Multi-stage confirmation system
- **`rate_limiter.py`** - Token bucket rate limiter
- **`bridge_cli.py`** - CLI management tool
@ -129,7 +102,7 @@ cd mcp-multiagent-bridge
pip install mcp>=1.0.0
# Make executable
chmod +x agent_bridge_secure.py
chmod +x claude_bridge_secure.py
```
### 2. Configure MCP Server
@ -141,7 +114,7 @@ Add to `~/.claude.json`:
"mcpServers": {
"bridge": {
"command": "python3",
"args": ["/absolute/path/to/agent_bridge_secure.py"],
"args": ["/absolute/path/to/claude_bridge_secure.py"],
"env": {}
}
}
@ -180,16 +153,6 @@ See [YOLO_MODE.md](YOLO_MODE.md) and [SECURITY.md](SECURITY.md) for complete saf
## 📊 Statistics
**v1.1.0-production:**
- **Lines of Code:** ~6,700 (including production scripts)
- **Python Files:** 14 (8 core + 6 production scripts)
- **Documentation Files:** 11 (5 new: PRODUCTION.md + production scripts)
- **Test Coverage:** ✅ 482 operations validated, zero failures
- **Production Validation:** ✅ 10-agent stress test + 90-min S² test
- **Dependencies:** 1 (mcp>=1.0.0)
- **License:** MIT
**v1.0.0-beta:**
- **Lines of Code:** ~4,500 (including tests + docs)
- **Python Files:** 8
- **Documentation Files:** 6
@ -240,24 +203,12 @@ Special thanks to the Claude Code and MCP communities for inspiration and suppor
## 📈 Roadmap
### ✅ Completed (v1.1.0)
- ✅ Production hardening scripts
- ✅ Keep-alive daemon reliability
- ✅ External watchdog monitoring
- ✅ Automated task reassignment
- ✅ Multi-agent stress testing (10 agents validated)
### 🚧 In Progress
- Web dashboard for monitoring
- Prometheus metrics export
- Connection pooling for 100+ agents
### 🔮 Future Enhancements
Future enhancements being considered:
- Message encryption at rest
- Docker sandbox for YOLO mode
- Web dashboard for monitoring
- OAuth/OIDC authentication
- Plugin system for custom commands
- WebSocket push notifications (eliminate polling)
See open [issues](../../issues) and [discussions](../../discussions) for details.

View file

@ -75,7 +75,7 @@ npm run build
### 1. Place YOLO module
Ensure `yolo_mode.py` is in the same directory as `agent_bridge_secure.py`.
Ensure `yolo_mode.py` is in the same directory as `claude_bridge_secure.py`.
### 2. Enable YOLO mode in conversation

View file

@ -11,7 +11,7 @@ from pathlib import Path
class BridgeCLI:
def __init__(self, db_path: str = "/tmp/agent_bridge_secure.db"):
def __init__(self, db_path: str = "/tmp/claude_bridge_secure.db"):
self.db_path = db_path
def list_conversations(self):

View file

@ -1,6 +1,6 @@
#!/usr/bin/env python3
"""
Secure Agent Multi-Agent Bridge
Secure Claude Code Multi-Agent Bridge
Production-lean MCP server with auth, redaction, and safety controls
"""
@ -696,13 +696,13 @@ Note: Your partner can see this result via check_messages"""
return [TextContent(type="text", text=f"❌ Error: {str(e)}")]
async def main(db_path: str = "/tmp/agent_bridge_secure.db"):
async def main(db_path: str = "/tmp/claude_bridge_secure.db"):
"""Run the secure MCP server"""
global bridge
bridge = SecureBridge(db_path)
from mcp.server.stdio import stdio_server
async with stdio_server() as (read_stream, write_stream):
await app.run(
read_stream,
@ -711,14 +711,8 @@ async def main(db_path: str = "/tmp/agent_bridge_secure.db"):
)
def run_cli(argv: Optional[Iterable[str]] = None) -> None:
"""Entry point used by direct execution and compatibility shims."""
if __name__ == "__main__":
import sys
args = list(argv if argv is not None else sys.argv[1:])
db_path = args[0] if args else "/tmp/agent_bridge_secure.db"
db_path = sys.argv[1] if len(sys.argv) > 1 else "/tmp/claude_bridge_secure.db"
print(f"Starting secure bridge with database: {db_path}", file=sys.stderr)
asyncio.run(main(db_path))
if __name__ == "__main__":
run_cli()

View file

@ -1,8 +0,0 @@
#!/usr/bin/env python3
"""Compatibility launcher for the secure agent bridge using the Claude naming."""
from agent_bridge_secure import run_cli
if __name__ == "__main__":
run_cli()

View file

@ -1,8 +0,0 @@
#!/usr/bin/env python3
"""Compatibility launcher for the secure agent bridge using the Codex naming."""
from agent_bridge_secure import run_cli
if __name__ == "__main__":
run_cli()

View file

@ -5,7 +5,7 @@ build-backend = "setuptools.build_meta"
[project]
name = "mcp-multiagent-bridge"
version = "1.0.0-beta"
description = "Production-ready Python MCP server for secure multi-agent coordination with 4-stage safeguards and rate limiting"
description = "Python MCP server for secure multi-agent coordination with 4-stage YOLO safeguards and rate limiting"
readme = "README.md"
license = {text = "MIT"}
authors = [
@ -34,9 +34,7 @@ Issues = "https://github.com/dannystocker/mcp-multiagent-bridge/issues"
Documentation = "https://github.com/dannystocker/mcp-multiagent-bridge#readme"
[project.scripts]
agent-bridge = "agent_bridge_secure:run_cli"
claude-bridge = "claude_mcp_bridge_secure:run_cli"
codex-bridge = "codex_mcp_bridge_secure:run_cli"
claude-bridge = "claude_bridge_secure:main"
bridge-cli = "bridge_cli:main"
[tool.bandit]

View file

@ -1,300 +0,0 @@
# MCP Bridge Production Hardening Scripts
Production-ready deployment tools for running MCP bridge at scale with multiple agents.
## Overview
These scripts solve common production issues when running multiple Claude sessions coordinated via MCP bridge:
- **Idle session detection** - Workers can miss messages when sessions go idle
- **Keep-alive reliability** - Continuous polling ensures 100% message delivery
- **External monitoring** - Watchdog detects silent agents and triggers alerts
- **Task reassignment** - Automated recovery when workers fail
- **Push notifications** - Filesystem watchers eliminate polling delay
## Scripts
### For Workers
#### `keepalive-daemon.sh`
Background daemon that polls for new messages every 30 seconds.
**Usage:**
```bash
./keepalive-daemon.sh <conversation_id> <worker_token>
```
**Example:**
```bash
./keepalive-daemon.sh conv_abc123def456 token_xyz789abc123 &
```
**Logs:** `/tmp/mcp-keepalive.log`
#### `keepalive-client.py`
Python client that updates heartbeat and checks for messages.
**Usage:**
```bash
python3 keepalive-client.py \
--conversation-id conv_abc123 \
--token token_xyz789 \
--db-path /tmp/claude_bridge_coordinator.db
```
#### `check-messages.py`
Standalone script to check for new messages.
**Usage:**
```bash
python3 check-messages.py \
--conversation-id conv_abc123 \
--token token_xyz789
```
#### `fs-watcher.sh`
Filesystem watcher using inotify for push-based notifications (<50ms latency).
**Requirements:** `inotify-tools` (Linux) or `fswatch` (macOS)
**Usage:**
```bash
# Install inotify-tools first
sudo apt-get install -y inotify-tools
# Run watcher
./fs-watcher.sh <conversation_id> <worker_token> &
```
**Benefits:**
- Message latency: <50ms (vs 15-30s with polling)
- Lower CPU usage
- Immediate notification when messages arrive
---
### For Orchestrator
#### `watchdog-monitor.sh`
External monitoring daemon that detects silent workers.
**Usage:**
```bash
./watchdog-monitor.sh &
```
**Configuration:**
- `CHECK_INTERVAL=60` - Check every 60 seconds
- `TIMEOUT_THRESHOLD=300` - Alert if no heartbeat for 5 minutes
**Logs:** `/tmp/mcp-watchdog.log`
**Expected output:**
```
[16:00:00] ✅ All workers healthy
[16:01:00] ✅ All workers healthy
[16:07:00] 🚨 ALERT: Silent workers detected!
conv_worker5 | session_b | 2025-11-13 16:02:45 | 315
[16:07:00] 🔄 Triggering task reassignment...
```
#### `reassign-tasks.py`
Task reassignment script triggered by watchdog when workers fail.
**Usage:**
```bash
python3 reassign-tasks.py --silent-workers "<worker_list>"
```
**Logs:** Writes to `audit_log` table in SQLite database
---
## Architecture
### Multi-Agent Coordination
```
┌─────────────────────────────────────────────────────────┐
│ ORCHESTRATOR │
│ │
│ • Creates conversations for N workers │
│ • Distributes tasks │
│ • Runs watchdog-monitor.sh (monitors heartbeats) │
│ • Triggers task reassignment on failures │
└─────────────────┬───────────────────────────────────────┘
┌───────────┴───────────┬───────────┬───────────┐
│ │ │ │
┌─────▼─────┐ ┌──────▼──────┐ ┌───▼───┐ ┌───▼───┐
│ Worker 1 │ │ Worker 2 │ │Worker │ │Worker │
│ │ │ │ │ 3 │ │ N │
│ │ │ │ │ │ │ │
└───────────┘ └─────────────┘ └───────┘ └───────┘
│ │ │ │
│ │ │ │
keepalive keepalive keepalive keepalive
daemon daemon daemon daemon
│ │ │ │
└──────────────┴────────────────┴──────────┘
Updates heartbeat every 30s
```
### Database Schema
The scripts use the following additional table:
```sql
CREATE TABLE IF NOT EXISTS session_status (
conversation_id TEXT PRIMARY KEY,
session_id TEXT NOT NULL,
last_heartbeat TEXT NOT NULL,
status TEXT DEFAULT 'active'
);
```
---
## Quick Start
### Setup Workers
On each worker machine:
```bash
# 1. Extract credentials from your conversation
CONV_ID="conv_abc123"
WORKER_TOKEN="token_xyz789"
# 2. Start keep-alive daemon
./keepalive-daemon.sh "$CONV_ID" "$WORKER_TOKEN" &
# 3. Verify running
tail -f /tmp/mcp-keepalive.log
```
### Setup Orchestrator
On orchestrator machine:
```bash
# Start external watchdog
./watchdog-monitor.sh &
# Monitor all workers
tail -f /tmp/mcp-watchdog.log
```
---
## Production Deployment Checklist
- [ ] All workers have keep-alive daemons running
- [ ] Orchestrator has external watchdog running
- [ ] SQLite database has `session_status` table created
- [ ] Rate limits increased to 100 req/min (for multi-agent)
- [ ] Logs are being rotated (logrotate)
- [ ] Monitoring alerts configured for watchdog failures
---
## Troubleshooting
### Worker not sending heartbeats
**Symptom:** Watchdog reports worker silent for >5 minutes
**Diagnosis:**
```bash
# Check if daemon is running
ps aux | grep keepalive-daemon
# Check daemon logs
tail -f /tmp/mcp-keepalive.log
```
**Solution:**
```bash
# Restart keep-alive daemon
pkill -f keepalive-daemon
./keepalive-daemon.sh "$CONV_ID" "$WORKER_TOKEN" &
```
### High message latency
**Symptom:** Messages taking >60 seconds to deliver
**Solution:** Switch from polling to filesystem watcher
```bash
# Stop polling daemon
pkill -f keepalive-daemon
# Start filesystem watcher (requires inotify-tools)
./fs-watcher.sh "$CONV_ID" "$WORKER_TOKEN" &
```
**Expected improvement:** 15-30s → <50ms latency
### Database locked errors
**Symptom:** `database is locked` errors in logs
**Solution:** Ensure SQLite WAL mode is enabled
```python
import sqlite3
conn = sqlite3.connect('/tmp/claude_bridge_coordinator.db')
conn.execute('PRAGMA journal_mode=WAL')
conn.close()
```
---
## Performance Metrics
Based on testing with 10 concurrent agents:
| Metric | Polling (30s) | Filesystem Watcher |
|--------|---------------|-------------------|
| Message latency | 15-30s avg | <50ms avg |
| CPU usage | Low (0.1%) | Very Low (0.05%) |
| Message delivery | 100% | 100% |
| Idle detection | 2-5 min | 2-5 min |
| Recovery time | <5 min | <5 min |
---
## Testing
Run the test suite to validate production hardening:
```bash
# Test keep-alive reliability (30 minutes)
python3 test_keepalive_reliability.py
# Test watchdog detection (5 minutes)
python3 test_watchdog_monitoring.py
# Test filesystem watcher latency (1 minute)
python3 test_fs_watcher_latency.py
```
---
## Contributing
See `CONTRIBUTING.md` in the root directory.
---
## License
Same as parent project (see `LICENSE`).
---
**Last Updated:** 2025-11-13
**Status:** Production-ready
**Tested with:** 10 concurrent Claude sessions over 30 minutes

View file

@ -1,72 +0,0 @@
#!/usr/bin/env python3
"""Check for new messages using MCP bridge"""
import sys
import sqlite3
import argparse
from datetime import datetime
from pathlib import Path
def check_messages(db_path: str, conversation_id: str, token: str):
"""Check for unread messages"""
try:
if not Path(db_path).exists():
print(f"⚠️ Database not found: {db_path}", file=sys.stderr)
return
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
# Get unread messages
cursor = conn.execute(
"""SELECT id, sender, content, action_type, created_at
FROM messages
WHERE conversation_id = ? AND read_by_b = 0
ORDER BY created_at ASC""",
(conversation_id,)
)
messages = cursor.fetchall()
if messages:
print(f"\n📨 {len(messages)} new message(s):")
for msg in messages:
print(f" From: {msg['sender']}")
print(f" Type: {msg['action_type']}")
print(f" Time: {msg['created_at']}")
content = msg['content'][:100]
if len(msg['content']) > 100:
content += "..."
print(f" Content: {content}")
print()
# Mark as read
conn.execute(
"UPDATE messages SET read_by_b = 1 WHERE id = ?",
(msg['id'],)
)
conn.commit()
print(f"{len(messages)} message(s) marked as read")
else:
print("📭 No new messages")
conn.close()
except sqlite3.OperationalError as e:
print(f"❌ Database error: {e}", file=sys.stderr)
sys.exit(1)
except Exception as e:
print(f"❌ Error: {e}", file=sys.stderr)
sys.exit(1)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Check for new MCP bridge messages")
parser.add_argument("--conversation-id", required=True, help="Conversation ID")
parser.add_argument("--token", required=True, help="Worker token")
parser.add_argument("--db-path", default="/tmp/claude_bridge_coordinator.db", help="Database path")
args = parser.parse_args()
check_messages(args.db_path, args.conversation_id, args.token)

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@ -1,63 +0,0 @@
#!/bin/bash
# S² MCP Bridge Filesystem Watcher
# Uses inotify to detect new messages immediately (no polling delay)
#
# Usage: ./fs-watcher.sh <conversation_id> <worker_token>
#
# Requirements: inotify-tools (Ubuntu) or fswatch (macOS)
DB_PATH="/tmp/claude_bridge_coordinator.db"
CONVERSATION_ID="${1:-}"
WORKER_TOKEN="${2:-}"
LOG_FILE="/tmp/mcp-fs-watcher.log"
if [ -z "$CONVERSATION_ID" ]; then
echo "Usage: $0 <conversation_id> <worker_token>"
exit 1
fi
# Check if inotify-tools is installed
if ! command -v inotifywait &> /dev/null; then
echo "❌ inotify-tools not installed" | tee -a "$LOG_FILE"
echo "💡 Install: sudo apt-get install -y inotify-tools" | tee -a "$LOG_FILE"
exit 1
fi
if [ ! -f "$DB_PATH" ]; then
echo "⚠️ Database not found: $DB_PATH" | tee -a "$LOG_FILE"
echo "💡 Waiting for orchestrator to create conversations..." | tee -a "$LOG_FILE"
fi
echo "👁️ Starting filesystem watcher for: $CONVERSATION_ID" | tee -a "$LOG_FILE"
echo "📂 Watching database: $DB_PATH" | tee -a "$LOG_FILE"
# Find helper scripts
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
CHECK_SCRIPT="$SCRIPT_DIR/check-messages.py"
KEEPALIVE_CLIENT="$SCRIPT_DIR/keepalive-client.py"
# Initial check
if [ -f "$DB_PATH" ]; then
python3 "$CHECK_SCRIPT" \
--conversation-id "$CONVERSATION_ID" \
--token "$WORKER_TOKEN" \
>> "$LOG_FILE" 2>&1
fi
# Watch for database modifications
inotifywait -m -e modify,close_write "$DB_PATH" 2>/dev/null | while read -r directory event filename; do
TIMESTAMP=$(date '+%Y-%m-%d %H:%M:%S')
echo "[$TIMESTAMP] 📨 Database modified, checking for new messages..." | tee -a "$LOG_FILE"
# Check for new messages immediately
python3 "$CHECK_SCRIPT" \
--conversation-id "$CONVERSATION_ID" \
--token "$WORKER_TOKEN" \
>> "$LOG_FILE" 2>&1
# Update heartbeat
python3 "$KEEPALIVE_CLIENT" \
--conversation-id "$CONVERSATION_ID" \
--token "$WORKER_TOKEN" \
>> "$LOG_FILE" 2>&1
done

View file

@ -1,85 +0,0 @@
#!/usr/bin/env python3
"""Keep-alive client for MCP bridge - polls for messages and updates heartbeat"""
import sys
import json
import argparse
import sqlite3
from datetime import datetime
from pathlib import Path
def update_heartbeat(db_path: str, conversation_id: str, token: str) -> bool:
"""Update session heartbeat and check for new messages"""
try:
if not Path(db_path).exists():
print(f"⚠️ Database not found: {db_path}", file=sys.stderr)
print(f"💡 Tip: Orchestrator must create conversations first", file=sys.stderr)
return False
conn = sqlite3.connect(db_path)
conn.row_factory = sqlite3.Row
# Verify conversation exists
cursor = conn.execute(
"SELECT role_a, role_b FROM conversations WHERE id = ?",
(conversation_id,)
)
conv = cursor.fetchone()
if not conv:
print(f"❌ Conversation {conversation_id} not found", file=sys.stderr)
return False
# Check for unread messages
cursor = conn.execute(
"""SELECT COUNT(*) as unread FROM messages
WHERE conversation_id = ? AND read_by_b = 0""",
(conversation_id,)
)
unread_count = cursor.fetchone()['unread']
# Update heartbeat (create session_status table if it doesn't exist)
conn.execute(
"""CREATE TABLE IF NOT EXISTS session_status (
conversation_id TEXT PRIMARY KEY,
session_id TEXT NOT NULL,
last_heartbeat TEXT NOT NULL,
status TEXT DEFAULT 'active'
)"""
)
conn.execute(
"""INSERT OR REPLACE INTO session_status
(conversation_id, session_id, last_heartbeat, status)
VALUES (?, 'session_b', ?, 'active')""",
(conversation_id, datetime.utcnow().isoformat())
)
conn.commit()
print(f"✅ Heartbeat updated | Unread messages: {unread_count}")
if unread_count > 0:
print(f"📨 {unread_count} new message(s) available - worker should check")
conn.close()
return True
except sqlite3.OperationalError as e:
print(f"❌ Database error: {e}", file=sys.stderr)
return False
except Exception as e:
print(f"❌ Error: {e}", file=sys.stderr)
return False
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="MCP Bridge Keep-Alive Client")
parser.add_argument("--conversation-id", required=True, help="Conversation ID")
parser.add_argument("--token", required=True, help="Worker token")
parser.add_argument("--db-path", default="/tmp/claude_bridge_coordinator.db", help="Database path")
args = parser.parse_args()
success = update_heartbeat(args.db_path, args.conversation_id, args.token)
sys.exit(0 if success else 1)

View file

@ -1,51 +0,0 @@
#!/bin/bash
# S² MCP Bridge Keep-Alive Daemon
# Polls for messages every 30 seconds to prevent idle session issues
#
# Usage: ./keepalive-daemon.sh <conversation_id> <worker_token>
CONVERSATION_ID="${1:-}"
WORKER_TOKEN="${2:-}"
POLL_INTERVAL=30
LOG_FILE="/tmp/mcp-keepalive.log"
DB_PATH="/tmp/claude_bridge_coordinator.db"
if [ -z "$CONVERSATION_ID" ] || [ -z "$WORKER_TOKEN" ]; then
echo "Usage: $0 <conversation_id> <worker_token>"
echo "Example: $0 conv_abc123 token_xyz456"
exit 1
fi
echo "🔄 Starting keep-alive daemon for conversation: $CONVERSATION_ID" | tee -a "$LOG_FILE"
echo "📋 Polling interval: ${POLL_INTERVAL}s" | tee -a "$LOG_FILE"
echo "💾 Database: $DB_PATH" | tee -a "$LOG_FILE"
# Find the keepalive client script
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
CLIENT_SCRIPT="$SCRIPT_DIR/keepalive-client.py"
if [ ! -f "$CLIENT_SCRIPT" ]; then
echo "❌ Error: keepalive-client.py not found at $CLIENT_SCRIPT" | tee -a "$LOG_FILE"
exit 1
fi
while true; do
TIMESTAMP=$(date '+%Y-%m-%d %H:%M:%S')
# Poll for new messages and update heartbeat
python3 "$CLIENT_SCRIPT" \
--conversation-id "$CONVERSATION_ID" \
--token "$WORKER_TOKEN" \
--db-path "$DB_PATH" \
>> "$LOG_FILE" 2>&1
RESULT=$?
if [ $RESULT -eq 0 ]; then
echo "[$TIMESTAMP] ✅ Keep-alive successful" >> "$LOG_FILE"
else
echo "[$TIMESTAMP] ⚠️ Keep-alive failed (exit code: $RESULT)" >> "$LOG_FILE"
fi
sleep $POLL_INTERVAL
done

View file

@ -1,63 +0,0 @@
#!/usr/bin/env python3
"""Task reassignment for silent workers"""
import sys
import sqlite3
import json
import argparse
from datetime import datetime
def reassign_tasks(silent_workers: str, db_path: str = "/tmp/claude_bridge_coordinator.db"):
"""Reassign tasks from silent workers to healthy workers"""
print(f"🔄 Reassigning tasks from silent workers...")
# Parse silent worker list (format: conv_id|session_id|last_heartbeat|seconds_since)
workers = [w.strip() for w in silent_workers.strip().split('\n') if w.strip()]
for worker in workers:
if '|' in worker:
parts = worker.split('|')
conv_id = parts[0].strip()
seconds_silent = parts[3].strip() if len(parts) > 3 else "unknown"
print(f"⚠️ Worker {conv_id} silent for {seconds_silent}s")
print(f"📋 Action: Mark tasks as 'reassigned' and notify orchestrator")
# In production:
# 1. Query pending tasks for this conversation
# 2. Update task status to 'reassigned'
# 3. Send notification to orchestrator
# 4. Log to audit trail
# For now, just log the alert
try:
conn = sqlite3.connect(db_path)
# Log alert to audit_log if it exists
conn.execute(
"""INSERT INTO audit_log (event_type, conversation_id, metadata, timestamp)
VALUES (?, ?, ?, ?)""",
(
"silent_worker_detected",
conv_id,
json.dumps({"seconds_silent": seconds_silent}),
datetime.utcnow().isoformat()
)
)
conn.commit()
conn.close()
print(f"✅ Alert logged to audit trail")
except sqlite3.OperationalError as e:
print(f"⚠️ Could not log to audit trail: {e}")
if __name__ == "__main__":
parser = argparse.ArgumentParser(description="Reassign tasks from silent workers")
parser.add_argument("--silent-workers", required=True, help="List of silent workers")
parser.add_argument("--db-path", default="/tmp/claude_bridge_coordinator.db", help="Database path")
args = parser.parse_args()
reassign_tasks(args.silent_workers, args.db_path)

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@ -1,58 +0,0 @@
#!/bin/bash
# S² MCP Bridge External Watchdog
# Monitors all workers for heartbeat freshness, triggers alerts on silent agents
#
# Usage: ./watchdog-monitor.sh
DB_PATH="/tmp/claude_bridge_coordinator.db"
CHECK_INTERVAL=60 # Check every 60 seconds
TIMEOUT_THRESHOLD=300 # Alert if no heartbeat for 5 minutes
LOG_FILE="/tmp/mcp-watchdog.log"
if [ ! -f "$DB_PATH" ]; then
echo "❌ Database not found: $DB_PATH" | tee -a "$LOG_FILE"
echo "💡 Tip: Orchestrator must create conversations first" | tee -a "$LOG_FILE"
exit 1
fi
echo "🐕 Starting S² MCP Bridge Watchdog" | tee -a "$LOG_FILE"
echo "📊 Monitoring database: $DB_PATH" | tee -a "$LOG_FILE"
echo "⏱️ Check interval: ${CHECK_INTERVAL}s | Timeout threshold: ${TIMEOUT_THRESHOLD}s" | tee -a "$LOG_FILE"
# Find reassignment script
SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
REASSIGN_SCRIPT="$SCRIPT_DIR/reassign-tasks.py"
while true; do
TIMESTAMP=$(date '+%Y-%m-%d %H:%M:%S')
# Query all worker heartbeats
SILENT_WORKERS=$(sqlite3 "$DB_PATH" <<EOF
SELECT
conversation_id,
session_id,
last_heartbeat,
CAST((julianday('now') - julianday(last_heartbeat)) * 86400 AS INTEGER) as seconds_since
FROM session_status
WHERE seconds_since > $TIMEOUT_THRESHOLD
ORDER BY seconds_since DESC;
EOF
)
if [ -n "$SILENT_WORKERS" ]; then
echo "[$TIMESTAMP] 🚨 ALERT: Silent workers detected!" | tee -a "$LOG_FILE"
echo "$SILENT_WORKERS" | tee -a "$LOG_FILE"
# Trigger reassignment protocol
if [ -f "$REASSIGN_SCRIPT" ]; then
echo "[$TIMESTAMP] 🔄 Triggering task reassignment..." | tee -a "$LOG_FILE"
python3 "$REASSIGN_SCRIPT" --silent-workers "$SILENT_WORKERS" 2>&1 | tee -a "$LOG_FILE"
else
echo "[$TIMESTAMP] ⚠️ Reassignment script not found: $REASSIGN_SCRIPT" | tee -a "$LOG_FILE"
fi
else
echo "[$TIMESTAMP] ✅ All workers healthy" >> "$LOG_FILE"
fi
sleep $CHECK_INTERVAL
done

View file

@ -11,7 +11,7 @@ from pathlib import Path
import sys
sys.path.insert(0, str(Path(__file__).parent))
from agent_bridge_secure import SecureBridge, SecretRedactor
from claude_bridge_secure import SecureBridge, SecretRedactor
def test_secret_redaction():

View file

@ -122,7 +122,7 @@ def test_integration():
print("\nTesting integration...")
try:
from agent_bridge_secure import SecureBridge, RATE_LIMITER_AVAILABLE
from claude_bridge_secure import SecureBridge, RATE_LIMITER_AVAILABLE
if not RATE_LIMITER_AVAILABLE:
print(" ❌ Rate limiter not integrated into SecureBridge")