Python MCP server for secure multi-agent coordination — 4-stage YOLO safeguards, auditable logs, and rate limiting
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🤖 Generated with [Claude Code](https://claude.com/claude-code)

Co-Authored-By: Claude <noreply@anthropic.com>
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bridge_cli.py Initial commit: Claude Code Bridge 2025-10-27 00:01:05 +01:00
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YOLO_MODE.md Initial commit: Claude Code Bridge 2025-10-27 00:01:05 +01:00
yolo_mode.py security: Implement critical safeguards before public release 2025-10-27 00:50:18 +01:00

MCP Multiagent Bridge

Production-ready Python MCP server for secure multi-agent coordination with comprehensive safeguards.

Overview

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.

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

Key Features

🔒 Security Architecture

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

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)

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

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)

Installation

# Clone repository
git clone https://github.com/dannystocker/mcp-multiagent-bridge.git
cd mcp-multiagent-bridge

# Install dependencies
pip install mcp>=1.0.0

# Run tests
python test_security.py

Full setup: See QUICKSTART.md


Documentation

Getting Started:

Security & Compliance:

  • SECURITY.md - Threat model, responsible disclosure policy
  • YOLO_MODE.md - Command execution safety guide
  • Policy compliance: Anthropic AUP, OpenAI Usage Policies

Contributing:


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: ~5,200 (including tests + documentation)
  • Test Coverage: Core security components verified
  • Documentation: 2,000+ lines across 7 markdown files
  • Dependencies: 1 (mcp, pinned for reproducibility)
  • License: MIT

Development

# Install dev dependencies
pip install -r requirements.txt

# Install pre-commit hooks
pip install pre-commit
pre-commit install

# Run test suite
pytest

# Run security tests
python test_security.py

See CONTRIBUTING.md for complete development workflow.


Security Notice

⚠️ Beta Software: Designed for development/testing environments with human supervision.

Recommended for:

  • Development and testing workflows
  • Isolated workspaces
  • Human-supervised operations
  • Prototype multi-agent systems

Not recommended for:

  • Production systems without additional safeguards
  • Unattended automation
  • Critical infrastructure
  • Environments with untrusted agents

See SECURITY.md for complete security considerations and threat model.


Support


License

MIT License - Copyright © 2025 Danny Stocker

See LICENSE for full terms.


Acknowledgments

Built with Claude Code and Model Context Protocol.