Agent-Reach

Agent Reach is a Python-based MCP server that helps you equip your AI agents with internet access and web-enabled capabilities by orchestrating a suite of upstream tools through MCP channels. It serves as a scaffold that plugs into various platforms (web pages, YouTube, RSS, GitHub, Twitter, Reddit, Xiaohongshu, Douyin, LinkedIn, Boss direct hiring, WeChat articles, and more) via MCP-enabled channels, enabling agents to perform tasks like reading pages with Jina Reader, extracting captions from videos with yt-dlp, performing semantic search with Exa via mcporter, and interacting with GitHub through the gh CLI, all without requiring bespoke one-off configurations for each platform. The server provides a doctor command to diagnose channel availability and readiness, and you can install or upgrade Agent Reach through a straightforward CLI workflow. The tools are designed to be pluggable: if you’re unhappy with a channel, you can swap out the corresponding channel implementation without affecting others. To use it, install the package, start the MCP server, then configure your agent to interact with the MCP endpoints.

Typical workflows involve: installing the agent-reach CLI, letting the server detect and install core dependencies (Node.js, gh CLI, mcporter, xreach, etc.), registering MCP-integrated SKILL guides, and then using the agent to call into read-web, read-tweet, fetch-RSS, search the web, or read GitHub repos via the exposed MCP channels. You can trigger platform-specific actions via Agent commands such as “帮我登录 GitHub” for GitHub, or “帮我配代理” for proxy-enabled channels, and the server will route to the appropriate upstream tool. The included doctor command summarizes the health of each channel and guides you through enabling or troubleshooting steps.

Note that for platforms requiring credentials (cookies), the project emphasizes local storage of credentials and safe operation modes; you can run in a safe mode where no system packages are auto-installed, and you can perform a dry-run to preview actions before changes are made.

Prerequisites:

• Python 3.10+ installed on your machine
• Basic command-line tools (bash/zsh) and network access
• Optional: a virtual environment tool (venv) if you prefer isolated environments

Installation steps:

1. Create and activate a Python virtual environment (optional but recommended)

   • macOS/Linux: python3 -m venv venv source venv/bin/activate
   • Windows (PowerShell): python -m venv venv .\venv\Scripts\Activate.ps1

2. Install the Agent Reach package from PyPI

   • pip install --upgrade pip
   • pip install agent-reach

3. Verify installation

   • agent-reach --version # or run the Python module to ensure it is accessible

4. Start the MCP server (as a background service or foreground, per your setup)

   • python -m agent_reach
   • If you prefer explicit invocation, ensure the module exposes an entry point to start the MCP server and listen on your desired port.

5. Optional: run a diagnosis to confirm channel health

   • agent-reach doctor

6. Integrate with your MCP ecosystem

   • Ensure your MCP configuration points to the running Python MCP server (see mcp_config example below) and restart any dependent agents if necessary.

Tips and common considerations:

• Credentials: For platforms requiring login cookies, use local storage (e.g., ~/.agent-reach/config.yaml) and keep permissions strict (chmod 600). Use the safe mode if deploying in shared environments.
• Dry run: Use --dry-run to preview actions without making changes. This is useful when validating new channels or updates.
• Modularity: If a channel (e.g., Xiaohongshu, Twitter) becomes unreliable, you can swap its channel file in the MCP setup to point to an alternative upstream tool with minimal impact on other channels.
• Security: The project emphasizes open-source transparency and local credential handling; ensure you review the code paths for any channel integrations you enable.
• Documentation: The MCP integration relies on SKILL.md and doctor outputs to guide usage. Keep SKILL definitions up to date to reflect any changes in upstream tools.