Docs Search
Enables LLMs to dynamically search and retrieve up-to-date documentation from popular AI libraries.
About
This lightweight MCP server empowers Language Models to query and fetch up-to-date documentation content dynamically from libraries like LangChain, LlamaIndex, and OpenAI. It uses a combination of web search via the Serper API and HTML parsing with BeautifulSoup to extract relevant documentation, providing LLMs with a scalable, plug-and-play interface to access real-world documentation services.
Key Features
- Web search integration for documentation retrieval
- Exposes a 'get_docs' tool for LLM agents
- Supports LangChain, LlamaIndex, and OpenAI libraries
- 0 GitHub stars
- Clean content extraction from HTML pages
- Uses the Model Context Protocol (MCP) for standardized LLM communication
Use Cases
- Providing LLMs with context from specific libraries to improve response accuracy
- Allowing LLMs to access up-to-date documentation in real-time
- Creating a modular and scalable system for LLMs to interact with external tools
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