AI Development Guidelines MCP Server

# AI Development Guidelines MCP Server An MCP (Model Context Protocol) server that provides AI agents with professional development guidelines, coding standards, and best practices. The server uses an AI orchestration layer powered by Anthropic Claude to intelligently select and deliver the right documentation based on incoming agent requests. ## Features - **Professional Coding Rules**: Comprehensive standards for writing production-quality code - **Development Skills**: Best practices for problem-solving, debugging, testing, and more - **AI Steering Instructions**: Context-aware guidance for AI agents - **AI Orchestration**: Intelligent document selection using Claude - **MCP Protocol**: Standard protocol for AI agent communication ## Architecture The server implements the Model Context Protocol and provides: 1. **Resources**: Documentation files accessible via MCP resource URIs 2. **Tools**: Four main tools for retrieving guidelines: - `get_coding_rules`: Professional coding standards - `get_development_skills`: Development best practices - `get_steering_instructions`: AI agent guidance - `get_custom_guidance`: AI-curated context-specific advice ## Installation ### Prerequisites - Python 3.11+ - Anthropic API key (optional, but required for `get_custom_guidance` tool) ### Setup 1. Clone this repository 2. Install dependencies: ```bash pip install -r requirements.txt ``` or with uv: ```bash uv sync ``` 3. **(Optional)** Set your Anthropic API key for AI-powered custom guidance: ```bash export ANTHROPIC_API_KEY="your-api-key-here" ``` **Note**: The server works without an API key, but the `get_custom_guidance` tool will return a graceful error message directing users to the other three tools. The static documentation tools (`get_coding_rules`, `get_development_skills`, `get_steering_instructions`) work fully without any API key. ## Usage ### Running the MCP Server ```bash python main.py ``` The server runs as an MCP stdio server, communicating over standard input/output. ### MCP Client Configuration To use this server with an MCP client (like Claude Desktop), add it to your MCP configuration: ```json { "mcpServers": { "ai-dev-guidelines": { "command": "python", "args": ["/path/to/this/repo/main.py"], "env": { "ANTHROPIC_API_KEY": "your-api-key" } } } } ``` ### Available Tools #### 1. get_coding_rules Get professional coding rules and standards for writing production-quality code. ```python # No parameters required result = await session.call_tool("get_coding_rules", {}) ``` #### 2. get_development_skills Get development skills, best practices, and professional techniques. ```python # No parameters required result = await session.call_tool("get_development_skills", {}) ``` #### 3. get_steering_instructions Get AI agent steering instructions for context-aware development. ```python # No parameters required result = await session.call_tool("get_steering_instructions", {}) ``` #### 4. get_custom_guidance Get AI-curated guidance tailored to your specific development context. ```python # Requires query parameter result = await session.call_tool("get_custom_guidance", { "query": "How do I implement secure authentication in a Python web app?", "context": "Building a Flask application with user login" # optional }) ``` ### Available Resources The server exposes three documentation resources: - `guidelines://rules` - Professional Coding Rules - `guidelines://skills` - Development Skills & Practices - `guidelines://steering` - AI Steering Instructions ## Configuration Edit `config.yaml` to customize: - Server name and version - Documentation file paths - AI model settings (model, max_tokens, temperature) - Tool descriptions ## Documentation The server includes three main documentation files in the `docs/` directory: - **rules.md**: Professional coding standards, security practices, testing requirements - **skills.md**: Development skills from debugging to API design - **steering.md**: AI agent guidance for effective code generation You can customize these documents to match your organization's standards. ## Project Structure ``` . ├── main.py # Entry point ├── config.yaml # Configuration ├── src/ │ ├── mcp_server.py # Main MCP server implementation │ ├── ai_orchestrator.py # AI-powered context selector │ └── utils/ │ ├── config.py # Configuration management │ └── document_loader.py # Documentation file loader ├── docs/ │ ├── rules.md # Coding rules │ ├── skills.md # Development skills │ └── steering.md # AI steering └── README.md ``` ## How It Works 1. **Agent Request**: An AI agent calls one of the MCP tools 2. **Document Loading**: The server loads relevant documentation from markdown files 3. **AI Orchestration** (for custom guidance): Claude analyzes the query and selects relevant content 4. **Response**: The server returns targeted, actionable guidance ## Development ### Running Tests ```bash pytest ``` ### Adding New Documentation 1. Create or edit markdown files in `docs/` 2. Update `config.yaml` to reference new files 3. Restart the server ### Customizing AI Behavior Edit the system prompts in `src/ai_orchestrator.py` to change how the AI selects and presents documentation. ## Environment Variables - `ANTHROPIC_API_KEY`: Required for AI orchestration features ## License MIT ## Contributing Contributions are welcome! Please feel free to submit pull requests or open issues. ## Support For issues or questions, please open a GitHub issue.