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    Forge Mcp

    MCP server for project scaffolding agent

    0 stars
    Python
    Updated Jul 15, 2025
    autonomous-agents
    mcp
    mcp-server

    Table of Contents

    • Overview
    • Features
    • Prerequisites
    • Installation
    • Development
    • Running the Server
    • API Reference
    • query_agent
    • Integration with MCP Hosts
    • Integrating Cursor or Windsurf
    • Integrating Claude Desktop
    • Integrating VSCode
    • Contributing
    • Development Guidelines
    • License

    Table of Contents

    • Overview
    • Features
    • Prerequisites
    • Installation
    • Development
    • Running the Server
    • API Reference
    • query_agent
    • Integration with MCP Hosts
    • Integrating Cursor or Windsurf
    • Integrating Claude Desktop
    • Integrating VSCode
    • Contributing
    • Development Guidelines
    • License

    Documentation

    Forge MCP Server

    Python

    MCP

    MCP Server

    A Model Context Protocol (MCP) server for the Forge project scaffolding agent. This server acts as a bridge between Large Language Models (LLMs) and the Forge API, enabling AI assistants to scaffold new projects based on user requirements.

    MCP Inspector Screenshot

    Overview

    Forge MCP Server uses the FastMCP framework to create tools that LLMs can use to interact with the Forge project scaffolding API. This allows AI assistants to generate project structures, boilerplate code, and configurations based on natural language descriptions.

    Features

    • Easy integration with LLMs using the Model Context Protocol
    • Connection to the Forge project scaffolding API
    • Simple query interface for project generation
    • Error handling and timeout management

    Prerequisites

    • Python 3.13+
    • Pipenv

    Installation

    Clone the repository:

    bash
    git clone https://github.com/believemanasseh/forge-mcp.git
    cd forge-mcp

    Install dependencies:

    bash
    pipenv --python 3.13
    source .venv/bin/activate
    pipenv install

    Development

    Running the Server

    bash
    fastmcp dev server.py

    The server uses stdio for communication by default, making it compatible with various LLM integration frameworks.

    API Reference

    query_agent

    Sends request queries to the Forge API and returns the results.

    Parameters:

    • query (string): Natural language description of the project to scaffold

    Returns:

    • JSON response from the Forge API

    Example:

    txt
    query_agent("Create a Django project named deet.")

    Integration with MCP Hosts

    Integrating Cursor or Windsurf

    Update your configuration files at:

    • Cursor: ~/.cursor/mcp.json or .cursor/mcp.json
    • Windsurf: ~/.codeium/windsurf/mcp_config.json
    json
    {
        "mcpServers": {
            "forge-mcp": {
                "command": "pipenv",
                "args": ["run", "fastmcp", "run", "server.py"],
                "transport": "stdio"
            }
        }
    }

    Integrating Claude Desktop

    In Claude Desktop, go to Settings > Developer > Edit Config. Replace the configuration with:

    json
    {
        "mcpServers": {
            "forge-mcp": {
                "command": "pipenv",
                "args": [
                    "run",
                    "fastmcp",
                    "run",
                    "server.py"
                ],
                "cwd": "/path/to/your/forge-mcp"
            }
        }
    }

    *Note: Replace /path/to/your/forge-mcp with the absolute path to your cloned forge-mcp directory.*

    Integrating VSCode

    Update the VSCode workspace configuration file at .vscode/mcp.json:

    json
    {
        "servers": {
            "forge-mcp": {
                "type": "stdio",
                "command": "pipenv",
                "args": ["run", "fastmcp", "run", "server.py"],
            }
        }
    }

    Contributing

    We welcome contributions to the Forge MCP Server project! Here's how you can contribute:

    1. Fork the repository - Create your own fork of the project

    2. Create a feature branch - git checkout -b feature/your-feature-name

    3. Commit your changes - Make sure to write clear commit messages

    4. Push to your branch - git push origin feature/your-feature-name

    5. Open a pull request - Describe the changes you've made and why they should be included

    Development Guidelines

    • Follow PEP 8 style guidelines for Python code
    • Write docstrings for all functions, classes, and methods
    • Add appropriate unit tests for new features
    • Update documentation to reflect any changes

    License

    Apache License 2.0

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