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Swarms MCP Documentation Server

@Ransom-Alpha

代码仓库

基本信息

分类

记忆与知识

传输方式

stdio

作者

Ransom-Alpha

关于 Swarms MCP Documentation Server

MCP server to connect AI agents to any github corpa

#swarms#mcp#ai-agents

Swarms MCP Documentation Server 配置

使用下面的配置,将此服务器添加到你的 MCP 客户端。

{
  "mcpServers": {
    "Swarms_MCPserver": {
      "command": "python",
      "args": [
        "swarms_server.py",
        "--reindex"
      ]
    }
  }
}

Swarms MCP Documentation Server 工具

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。

概览

What is 🐝 Swarms MCP Documentation Server?

An MCP server built on FastMCP that enables AI agents to efficiently retrieve information from local documentation repositories. It uses hybrid semantic (vector) and keyword (BM25) search, chunked indexing, and FastMCP tools for seamless agent integration. Designed for developers working with agent frameworks like Swarms, Autogen, LangGraph, and OpenAI Agent SDK.

How to use 🐝 Swarms MCP Documentation Server?

Clone documentation repos into the corpora/ folder, install Python 3.11 dependencies from requirements.txt, set an OPENAI_API_KEY in .env, run python embed_documents.py to load and embed documents, then start the server with python swarms_server.py. For IDE integration, add a Windsurf Cascade configuration pointing to the server script and access tools like swarm_docs.search.

Key features of 🐝 Swarms MCP Documentation Server

  • Hybrid retriever combining semantic and keyword search
  • Dynamic Markdown loading based on file size
  • Specialized loaders for .py, .ipynb, .md, .txt, .yaml, .yml
  • Live file watchdog that auto-reindexes on changes to corpora/
  • Healthcheck endpoint to verify server readiness
  • User confirmation before expensive embedding operations

Use cases of 🐝 Swarms MCP Documentation Server

  • AI agents searching large framework documentation for relevant code chunks
  • Listing all indexed files across multiple cloned codebases
  • Retrieving specific document chunks by path and index for context
  • Forcing a full or incremental reindex when documentation changes
  • Checking server health before integrating with an agent pipeline

常见问题

1

Why do I get 'No valid existing index found' when starting the server?

The server will prompt you to embed and index documents automatically. Accept the prompt, or run `python embed_documents.py` manually first.

2

Which index file is used for Chroma vector storage?

The server prefers `chroma-collections.parquet` if available; otherwise it falls back to `chroma.sqlite3`.

3

How can I force a reindex of the documentation?

Run `python swarms_server.py --reindex` or use the `swarm_docs.reindex` tool. For a full schema refresh, add `--full`.

4

How do I add new documentation or repos?

Place the new repository or files inside the `corpora/` folder, then reindex with the `--reindex` flag or the corresponding tool.

5

What are the runtime requirements?

Python 3.11 environment with dependencies from `requirements.txt`, an OpenAI API key, and local storage for the Chroma vector database. No external cloud services are required beyond the embedding API call.

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