Implement Model Context Protocol (MCP) Support #37
Description
Description
Implement support for Anthropic's Model Context Protocol (MCP) in DoogieBot, allowing administrators to configure and manage various MCP servers using Docker containers. This feature will enable users to extend the chatbot's capabilities by connecting it to external data sources and tools through a standardized protocol.
Background
Model Context Protocol (MCP) is an open standard developed by Anthropic that provides standardized interfaces for LLM applications to integrate with external data sources and tools. By implementing MCP support, DoogieBot will be able to access various data sources and integrate with different tools, enhancing its capabilities.
Requirements
- Add ability to configure MCP servers with Docker containers
- Support various MCP server types (filesystem, git, GitHub, PostgreSQL, etc.)
- Implement Docker-in-Docker functionality for container management
- Provide a user-friendly admin interface for managing MCP configurations
- Ensure proper security measures for Docker operations
- Implement comprehensive test coverage
Implementation Plan
Phase 1: Testing Infrastructure
- Create Docker API mocking framework
- Set up test structure for Docker API
- Create test fixtures for MCP configurations
- Implement test cases for Docker configuration validation
Phase 2: Database Models and Schemas
- Create MCP configuration database models
- Define Pydantic schemas for API validation
- Create Docker configuration models
- Generate and test database migrations
Phase 3: Services Implementation
- Implement Docker service layer (containers, images, volumes, networks)
- Create MCP configuration service
- Implement MCP server management service
- Ensure proper error handling and logging
Phase 4: API Routes Implementation
- Create Docker API endpoints
- Implement MCP configuration CRUD endpoints
- Add MCP server management endpoints (start, stop, restart)
- Ensure proper authentication and authorization
Phase 5: Docker Configuration and Utilities
- Implement Docker configuration utilities (YAML/JSON parsers)
- Update Dockerfile with Docker-in-Docker support
- Modify docker-compose.yml for Docker socket mounting
- Create setup scripts for Docker-in-Docker environment
Phase 6: Frontend Implementation
- Create MCP configuration management components
- Implement Docker management components
- Build MCP configuration pages (list, create, edit)
- Add Docker management dashboard
- Implement frontend services for API communication
Phase 7: Documentation
- Create MCP configuration guide
- Document Docker API endpoints
- Provide developer documentation
- Update user manual
Technical Considerations
- Docker socket security: Mounting the Docker socket inside the container has security implications
- Authentication: Ensure only admin users can manage Docker containers
- Resource limitations: Implement container resource constraints
- Error handling: Robust error handling for Docker API failures
- Monitoring: Add monitoring for running MCP servers
Docker-in-Docker Changes
# Dockerfile additions:
RUN apt-get update && apt-get install -y docker-ce-cli
# docker-compose.yml additions:
volumes:
- /var/run/docker.sock:/var/run/docker.sock
environment:
- DOCKER_HOST=unix:///var/run/docker.sock
Dependencies
- Docker Python SDK (docker-py)
- SQLAlchemy for database models
- FastAPI for API endpoints
- React components for frontend
Acceptance Criteria
- Administrators can create, update, and delete MCP server configurations
- MCP servers can be started, stopped, and restarted through the admin interface
- Docker containers, images, volumes, and networks can be managed
- Configuration can be exported in standard MCP JSON format
- All operations are properly secured with admin-only access
- Test coverage is at least 90% for all new code
- Documentation is comprehensive and up-to-date
Related Issues
- None yet (will be linked as they are created)