Code Generator MCP Server

Code Generator MCP Template Server

An MCP (Model Context Protocol) server built using Python's FastMCP framework. It exposes 4 precise, structured code-generation tools backed by prompt templates to generate production-grade, parsed code using local or cloud-based OpenAI-compatible APIs (such as llama-server, qwen-coder, coder-expert, or OpenAI's API).

🚀 Features

Exposes 4 key MCP tools that use structured templates to instruct the model to think step-by-step and produce clean, executable code:

1. generate_standard_function (Template 1): Generates a standalone function based on constraints, edge cases, test cases, and external integration notes.
2. generate_codebase_context (Template 2): Generates a function that respects and integrates with existing codebase structures and dependencies.
3. generate_bugfix_refactor (Template 3): Focuses on refactoring or repairing current buggy implementations based on problem descriptions and test expectations.
4. generate_multi_function_module (Template 4): Generates a multi-function module, validating that there are no circular dependencies or undefined functions.

⚙️ Parser & Guardrails

• Markdown Stripper: Automatic code parsing (extract_code_from_response) strips any markdown code blocks ( `python ) generated by instruct models, guaranteeing only raw executable code is returned.
• Reasoning Fallback: Correctly handles DeepSeek-style reasoning models or llama-server configurations where all output is redirected into the reasoning_content field instead of content.
• Max Tokens Guardrail: Enforces a 2048 token limit per request to prevent local model reasoning loops and timeouts.

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🛠️ Configuration

Configure the server using command-line arguments or environment variables:

| Setting | CLI Argument | Environment Variable | Default Value | Description | |---|---|---|---|---| | API URL | --api-url | CODE_GEN_API_URL / OPENAI_BASE_URL | https://api.openai.com/v1 | OpenAI-compatible endpoint | | Model | --model | CODE_GEN_MODEL / OPENAI_MODEL | gpt-4o | The model name to target | | API Key | --api-key | CODE_GEN_API_KEY / OPENAI_API_KEY | (Empty) | API token (optional for local endpoints) |

[!WARNING] For security reasons, do not pass --api-key via command-line arguments as it will be visible in plain text in the host process table. Use the environment variables instead.

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📦 Installation & Setup

You can install and configure the server either automatically using the installation script or manually via Python.

Option 1: Quick Installation Script (Recommended)

The project includes an install.sh script that automatically builds a standalone executable using PyInstaller, installs it to ~/.local/bin/code-generator-mcp, and configures your target AI coding agent.

1. Run the script and specify your target agent:

   chmod +x install.sh
   ./install.sh <agent_type>

Supported <agent_type> values:

• claude-desktop (Claude Desktop global configuration)
• claude-code (Claude CLI global configuration at ~/.claude.json)
• cursor (Cursor editor global config at ~/.cursor/mcp.json)
• codex (Codex agent global config at ~/.codex/config.toml)
• github-copilot (VS Code workspace-specific config at .vscode/mcp.json)
• windsurf (Windsurf IDE configuration at ~/.codeium/windsurf/mcp_config.json)
• zed (Zed editor config at ~/.config/zed/settings.json)
• agy (Antigravity settings config at ~/.gemini/settings.json)

1. (Optional) Customize the endpoint and model in the agent's configuration file or environment variables after installation.

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Option 2: Manual Setup via Python

Prerequisites

• Python 3.10+
• Dependencies installed in virtual environment:

  python -m venv .venv
  source .venv/bin/activate
  pip install -r requirements.txt

Running Locally

To run the MCP server directly over standard input/output (stdio): ``bash python src/code_generator_mcp/server.py --api-url http://localhost:8008/v1 --model coder-expert ``

Testing during development

You can use mcp dev (from MCP CLI) to test the server interactively in a development UI: ``bash mcp dev src/code_generator_mcp/server.py -- --api-url http://localhost:8008/v1 --model coder-expert ``

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🔌 Integration Setup

To manually use this server with your favorite MCP client (like Claude Desktop or Cursor):

Claude Desktop Configuration

Open your Claude Desktop config file (usually located at ~/.config/Claude/claude_desktop_config.json on Linux/macOS or %APPDATA%\Claude\claude_desktop_config.json on Windows) and add the following entry:

{
  "mcpServers": {
    "code-generator-mcp": {
      "command": "/path/to/project/.venv/bin/python",
      "args": [
        "/path/to/project/src/code_generator_mcp/server.py"
      ],
      "env": {
        "CODE_GEN_API_URL": "http://localhost:8008/v1",
        "CODE_GEN_MODEL": "coder-expert"
      }
    }
  }
}

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🧪 Testing

The codebase includes a fully-featured unit and integration test suite using pytest. Run tests with: ``bash .venv/bin/pytest ``