iflow-mcp_draphonix_mcp-code-mode

# MCP Code Mode Prototype implementation for the Code Execution MCP Server with DSpy. The "Code Execution with MCP" architecture combines the strengths of Large Language Models at code generation with the Model Context Protocol for tool integration. This system enables an AI agent to write Python code that runs in an isolated sandbox while seamlessly calling external MCP tools. ## Quick Start ### 1. Installation Requires Python 3.11+ and Node.js 20+. ```bash # Create virtual environment python3.11 -m venv .venv source .venv/bin/activate # Install dependencies pip install -e .[dev] # Install Node.js dependencies for reference servers npm install -g npm@latest ``` ### 2. Configuration Copy the example environment file and configure secrets: ```bash cp .env.example .env ``` Configure your MCP servers in `mcp_servers.json`: ```json { "servers": { "filesystem": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-filesystem", "/your-working-folder"], "description": "Local file system operations" } } } ``` ### 3. Running the Server Launch the Code Execution MCP server: ```bash python -m mcp_code_mode.executor_server ``` ### 4. Verification Verify your setup by running the debug executor script. This script simulates an MCP client, connects to the server, and runs a test task to ensure the agent and tools are working correctly. Before running the script: 1. Configure the MCP servers you want to interact with in `mcp_servers.json`. 2. Define the specific task you want the agent to perform by editing the `task` variable in `scripts/debug_executor.py`. ```bash python scripts/debug_executor.py ``` ## Development Commands | Command | Description | |---------|-------------| | `pytest` | Run all tests | | `ruff check .` | Lint the codebase | | `black .` | Format the codebase | | `mypy src` | Type check the source | | `python scripts/test_dspy_sandbox.py` | Sanity check the sandbox | | `python scripts/debug_executor.py` | In…