Is MCP Base a ready-to-use MCP server?
No – it is a starter template. You add your own tools, prompts, and resources by creating classes and registering them in the provided registries.
@josharsh
基本信息
Boilerplate using one of the 'better' ways to build MCP Servers. Written using FastMCP
使用下面的配置,将此服务器添加到你的 MCP 客户端。
{
"mcpServers": {
"mcp-server-boilerplate": {
"command": "python",
"args": [
"main.py",
"--transport=stdio"
]
}
}
}工具列表暂未提供
当前收录暂时没有可展示的工具列表,不代表服务没有工具。请查阅项目文档了解支持的能力。
工具是从 README 中自动提取的。维护者可以在 ## Tools 标题下列出工具,即可填充这部分内容。
MCP Base is a production-ready, extensible Python template for building Model Context Protocol (MCP) servers. It provides a modular foundation for creating servers that expose tools, prompts, and resources to LLMs and agentic clients.
Install dependencies with pip install -r requirements.txt, copy .env.example to .env and configure, then run the server using python main.py --transport=stdio (STDIO) or follow transport-specific READMEs for SSE/HTTP.
No – it is a starter template. You add your own tools, prompts, and resources by creating classes and registering them in the provided registries.
MCP Base uses Python 3 and lists dependencies in `requirements.txt` and `pyproject.toml`. It is built on the official MCP Python SDK and uses Pydantic for schemas.
It supports STDIO, SSE, and HTTP transports. Each is modular and documented in separate READMEs under `/src/transports/`.
All file and directory operations are sandboxed to allowed paths. Input validation is enforced for all tool/resource inputs, and sensitive configuration is managed via environment variables.
Yes, contributions are welcome. See `CONTRIBUTING.md` for guidelines, code style, and the pull request process.
Modelglass turns "which model should I use?" into a lookup instead of a guess. Seven tools give your agent live pricing across image, language, video, and audio models. Sourced and versioned, not scraped once and forgott

Automated codebase documentation for GitHub: searchable docs, MCP context for AI agents, doc drift detection, and Friday Cleanup PRs.

Know when your AI agents, cron jobs and CI pipelines fail, stall or go quiet. Your agent sets up monitors from a plain description, tells you what is broken and why, reads run history and traces, and writes notes on inci

directree MCP server — query the honest software directory from any AI assistant

Let your cloud agents talk to you. Grok Bot, Meta Muse, ChatGPT, Claude, Devin and others report progress to Heard, which reads it out loud on your Mac, and they pick up the replies you send back.
评论