OpenRouter MCP Server

# Claude Code MCP CLI Management Guide ## Overview The OpenRouter MCP Server now includes a powerful CLI management system for Claude Code, allowing you to easily add, remove, and configure MCP servers using simple commands. ## Installation The MCP CLI management system is automatically included with the OpenRouter MCP Server installation. ## Usage ### Using the Standalone CLI You can use the `claude_mcp.py` script directly: ```bash python claude_mcp.py [COMMAND] [OPTIONS] ``` ### Available Commands #### 1. Add an MCP Server Add a new MCP server using presets or custom configuration: ```bash # Add OpenRouter server with API key python claude_mcp.py add openrouter --api-key sk-or-your-key-here # Add GitHub server with token python claude_mcp.py add github --env GITHUB_PERSONAL_ACCESS_TOKEN=ghp_xxx # Add filesystem server with directories python claude_mcp.py add filesystem --args /path/to/dir1 --args /path/to/dir2 # Add custom server python claude_mcp.py add myserver --command python --args server.py --cwd /project ``` Options: - `--api-key`: API key for servers that require authentication - `--force`: Force overwrite if server already exists - `--command`: Command to run the server (for custom servers) - `--args`: Arguments for the server command (can be used multiple times) - `--cwd`: Working directory for the server - `--env`: Environment variables in KEY=VALUE format (can be used multiple times) #### 2. List MCP Servers List all installed MCP servers: ```bash # Simple list python claude_mcp.py list # Verbose list with details python claude_mcp.py list --verbose ``` #### 3. Get Server Status Get detailed status of a specific MCP server: ```bash python claude_mcp.py status openrouter ``` #### 4. Configure a Server Update configuration for an existing MCP server: ```bash # Update API key python claude_mcp.py config openrouter --env OPENROUTER_API_KEY=new-key # Update working directory python claude_mcp.py config myserver --cwd /new/path # Update arguments python claude_mcp.py config filesystem --args /new/dir1 --args /new/dir2 ``` #### 5. Remove a Server Remove an MCP server from Claude Code: ```bash python claude_mcp.py remove openrouter ``` ## Available Presets The following MCP servers are available as presets for easy installation: ### OpenRouter Provides access to multiple AI models through OpenRouter's API. ```bash python claude_mcp.py add openrouter --api-key YOUR_OPENROUTER_API_KEY ``` Required: OpenRouter API key from https://openrouter.ai ### Filesystem Provides file system access to specified directories. ```bash python claude_mcp.py add filesystem --args ~/Documents --args ~/Desktop ``` ### GitHub Provides GitHub repository access and management. ```bash python claude_mcp.py add github --env GITHUB_PERSONAL_ACCESS_TOKEN=ghp_xxx ``` Required: GitHub Personal Access Token ### Memory Provides in-memory storage capabilities. ```bash python claude_mcp.py add memory ``` ## Integration with Claude Code After adding MCP servers, restart Claude Code CLI to use the new tools. The configuration is automatically saved to: - **Windows**: `%USERPROFILE%\.claude\claude_code_config.json` - **macOS/Linux**: `~/.claude/claude_code_config.json` ## Configuration File Structure The MCP servers are stored in the Claude Code configuration file: ```json { "mcpServers": { "openrouter": { "command": "python", "args": ["-m", "src.openrouter_mcp.server"], "cwd": "/path/to/openrouter-mcp", "env": { "OPENROUTER_API_KEY": "sk-or-xxx", "HOST": "localhost", "PORT": "8000" } } } } ``` ## Python API You can also use the MCP Manager programmatically: ```python from src.openrouter_mcp.cli import MCPManager, MCPServerConfig # Create manager manager = MCPManager() # Add a server config = MCPServerConfig( name="myserver", command="python", args=["server.py"], env={"API_KEY": "secret"} ) manager.add_server(config) # List servers servers = manager.list_servers() print(f"Installed servers: {servers}") # Get server status status = manager.get_server_status("myserver") print(f"Server status: {status}") # Remove server manager.remove_server("myserver") ``` ## Troubleshooting ### Server Not Found If you get a "server not found" error, check that: 1. The server name is correct 2. The server has been added (use `list` command) ### Permission Errors If you encounter permission errors: 1. Ensure you have write access to the Claude config directory 2. Try running with appropriate permissions ### API Key Issues For servers requiring API keys: 1. Ensure the API key is valid 2. Check that environment variables are set correctly 3. Use the `config` command to update keys ## Advanced Features ### Backup and Restore The MCP Manager automatically creates backups when modifying configuration: ```python from src.openrouter_mcp.cli import MCPManager manager = MCPManager() # Create manual backup backup_path = manager.backup_config() print(f"Backup created: {backup_path}") # Restore from backup manager.restore_config(backup_path) ``` ### Cross-Platform Support The MCP CLI automatically handles platform-specific paths and configurations: - Windows paths are properly escaped - Home directory expansion works on all platforms - Environment variables are platform-appropriate ## Examples ### Complete Setup Example ```bash # 1. Add OpenRouter server python claude_mcp.py add openrouter --api-key sk-or-xxx # 2. Add filesystem access to Desktop python claude_mcp.py add filesystem --args ~/Desktop # 3. List all servers python claude_mcp.py list --verbose # 4. Restart Claude Code CLI # The MCP tools are now available! ``` ### Using in Claude Code After setup, you can use the MCP tools in Claude Code: ``` User: List available AI models Claude: I'll list the available models using the OpenRouter MCP tool... User: Read files from my Desktop Claude: I'll access your Desktop using the filesystem MCP tool... ``` ### Using Collective Intelligence Tools The OpenRouter MCP Server includes 5 advanced collective intelligence tools for enhanced AI collaboration: ```bash # Example: Multi-model consensus for complex decisions User: "Use collective intelligence to analyze the pros and cons of remote work with 3 different models" Claude: I'll use the collective_chat_completion tool to get consensus from multiple AI models... # Example: Ensemble reasoning for complex problems User: "Apply ensemble reasoning to design a sustainable energy solution for a small city" Claude: I'll use ensemble_reasoning to decompose this complex problem and assign different aspects to specialized models... # Example: Adaptive model selection for optimal performance User: "Automatically select the best model for writing a Python function to process large datasets" Claude: I'll use adaptive_model_selection to find the most suitable model for this coding task... # Example: Cross-model validation for accuracy User: "Validate this scientific statement across multiple models: 'Quantum computers will replace classical computers within 10 years'" Claude: I'll use cross_model_validation to verify this claim across multiple AI models... # Example: Collaborative problem solving User: "Solve this business challenge collaboratively: How to reduce customer churn by 30% in 6 months" Claude: I'll use collaborative_problem_solving to get multiple models working together on this challenge... ``` ### Performance Benefits The collective intelligence tools provide significant advantages: - **Higher Accuracy**: Multi-model consensus reduces individual model biases - **Better Quality**: Cross-validation ensures response reliability - **Optimal Performance**: Adaptive selection chooses the best model for each task - **Complex Problem Solving**: Ensemble reasoning handles multi-faceted challenges - **Reliability**: Collaborative approaches provide robust, well-reasoned solutions ## Support For issues or questions: 1. Check the [OpenRouter MCP documentation](https://github.com/yourusername/openrouter-mcp) 2. Review the [Model Context Protocol specification](https://modelcontextprotocol.io) 3. File an issue on GitHub ## License MIT License - See LICENSE file for details.