MCP Meeting Agent

MCP Meeting Agent

A meeting preparation agent that provides trivia, fun facts, and GitHub trending repositories to improve your meetings. Built with LangChain's modern agent framework for intelligent orchestration and tool-based interactions.

Proof of Concept

This is a proof of concept application and is not intended for production use. This project demonstrates architectural patterns and integration concepts but lacks critical production features such as security, performance optimization, comprehensive testing, and scalability considerations.

Features

• Tech Trivia: Fetches technology trivia questions and answers from external APIs
• Fun Facts: Provides interesting random facts from external APIs
• GitHub Trending: Shows current trending repositories from external APIs
• Meeting Notes: Generates formatted meeting notes for hosts
• LangChain Agent Framework: Modern LLM agent architecture with tool-based orchestration
• Intelligent Tool Coordination: Agent automatically selects and uses appropriate tools
• Robust Error Handling: Graceful fallbacks and comprehensive error recovery
• Structured Logging: Comprehensive logging with configurable levels
• Comprehensive Testing: Full test coverage for all components
• FastMCP Integration: Production-ready MCP server with error masking, rate limiting, and context-aware logging

Architecture

The project follows a modern LangChain agent architecture with clean separation of concerns and provider-agnostic LLM support:

Provider-Agnostic Design

The application is designed to work with multiple LLM providers without code changes:

• OpenAI/OpenRouter: Use any OpenAI-compatible API via base_url configuration
• Anthropic Claude: Direct support for Claude models (requires langchain-anthropic)
• Google Gemini: Direct support for Gemini models (requires langchain-google-genai)
• Other Providers: Any OpenAI-compatible API via base_url configuration

The system automatically detects the provider based on the model name and configuration, making it easy to switch between providers by simply updating your .env file.

• LangChain Agents: Intelligent orchestrators that coordinate tools using LLM reasoning
• Tools: Reusable LangChain tools that wrap external services and APIs
• Services: Handle external API interactions and data fetching
• Schemas: Pydantic models for data validation and structure
• Formatters: Format data for different output types (LLM, notes)
• Prompts: Manage LLM prompt templates
• Core: Configuration, logging, and LLM gateway

Key Architectural Benefits

• Tool-Based Design: Individual tools can be reused across different agents
• Intelligent Orchestration: Agent uses LLM reasoning to determine which tools to use
• Better Error Handling: Tools have individual error handling with graceful fallbacks
• Flexibility: Easy to add new tools without changing agent logic
• Modern LLM Patterns: Follows LangChain's recommended agent-tool architecture

Quick Start

1. Clone the repository:
   git clone https://github.com/cliffordru/mcp-meeting-agent cd mcp-meeting-agent
2. Install dependencies:
   uv sync
   Optional: Install additional LLM provider support:
   # For Anthropic Claude support uv add langchain-anthropic # For Google Gemini support uv add langchain-google-genai # Or install all providers uv add langchain-anthropic langchain-google-genai
3. Set up environment variables:
   cp env.example .env # Edit .env with your preferred settings
4. Run the MCP Server:
   uv run server.py
5. Configure MCP Client: To use this MCP server with your MCP host client, see mcp-client-config.json in the project root for the complete configuration example.The server runs on http://127.0.0.1:8000/sse by default and provides the prepare_meeting() tool for generating meeting content.
6. Use: For example in Cursor, you can prompt your AI assistant "Using your MCP tools, prepare some fun meeting notes".

Configuration

Key configuration options in .env:

Testing

Run all tests:

Run with coverage:

Production Readiness Considerations

Testing & Quality Assurance

Current State: Comprehensive unit tests with good coverage Production Needs:

• Load Testing: Apache Bench, Artillery, or k6 for API performance testing
• Integration Testing: End-to-end testing with real API dependencies
• Failure Scenario Testing: Network failures, API rate limits, LLM timeouts
• Evaluation Framework: TBD
• Monitoring: Langfuse integration for LLM performance tracking

Security Considerations

Current State: Basic FastMCP error masking and rate limiting implemented Production Needs:

• Authentication: OAuth 2.0 flow with proper session management
• Input Validation: Content filtering and sanitization for all inputs
• Output Filtering: LLM output validation to prevent harms
• SAST/DAST: Static and dynamic application security testing
• SCA: Software composition analysis for dependency vulnerabilities
• Rate Limiting: More sophisticated rate limiting and abuse prevention
• Secrets Management: TBD when needed

Performance & Scalability

Current State: Async implementation with LangChain agent framework Production Needs:

• Caching: TBD based on load testing
• Circuit Breakers: Resilience patterns for external API failures
• Horizontal Scaling: Container orchestration (Kubernetes/Docker)

AI Architecture Improvements

Current State: LangChain agent framework with tool-based architecture, LLM-generated fallback content for API failures, and provider-agnostic LLM support Production Needs:

• Model-as-a-Service: Right-sized models for cost/latency/accuracy balance
• Prompt Engineering: Systematic prompt optimization and versioning
• Agent Optimization: Fine-tune agent prompts and tool selection logic
• Multi-Provider Support: Provider switching and fallback mechanisms
• Tool Validation: Input/output validation for tools
• LLM-Generated Fallbacks: Improve dynamic LLM-generated content when APIs fail
  • Generate contextual trivia questions based on meeting type/context
  • Create relevant fun facts tailored to the audience/industry
  • Provide trending tech topics specific to the team's domain
  • Maintain content freshness and relevance through AI generation

Integration & Real-World Services

Current State: Basic external APIs with tool-based access Production Needs:

• GitHub Integration: Real-time issues, PRs, and repository health
• CI/CD Integration: Build status and deployment information
• Jira/Linear: Project management and sprint data
• Slack/Teams: Real-time notifications and team collaboration
• Calendar Integration: Meeting scheduling and participant management
• Analytics: Meeting effectiveness tracking and insights

Monitoring & Alerting

Current State: Basic structured logging with FastMCP client logging integration Production Needs:

• Observability & Alerting: Monitoring for agent performance and tool usage
• Centralized Logging: Log aggregation and analysis
• Performance Metrics: Response time tracking and alerting

Project Structure

API Endpoints

The MCP server exposes a single tool:

• prepare_meeting(ctx: Context, meeting_context: str = ""): Generates meeting preparation content including trivia, fun facts, and trending repositories using LangChain agent orchestration with error handling and context-aware logging

Dependencies

• FastMCP: MCP server framework
• aiohttp: Async HTTP client
• pydantic: Data validation
• structlog: Structured logging
• langchain: LLM integration and agent framework
• langchain-core: Core LangChain components
• langchain-openai: OpenAI integration for LangChain
• pytest: Testing framework
• pytest-asyncio: Async test support

Note: Langfuse observability is included in dependencies but not yet implemented in the current version.

License

This project is licensed under the MIT License - see the LICENSE file for details.