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praveenc

llmstxt-doc-search

by praveenc

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    • A
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      Aggregates documentation from multiple sources (llms.txt format or web scraping) and provides semantic search capabilities using vector embeddings and hybrid search for each documentation source.
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      Enables AI clients to search and retrieve up-to-date technical documentation for popular agent frameworks such as langchain, llama-index, autogen, agno, openai-agents-sdk, mcp-doc, camel-ai, and crew-ai by running site-scoped Google searches and extracting text from the resulting pages. It can be deployed locally over the stdio transport or remotely over SSE, and works with clients like Cline, Cursor, and custom Python clients.
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    • A
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    TDQS

    A3.7/5.0

    Scored across 7 tools

    Disambiguation4/5

    Each tool has a distinct role: search, fetch, list, add, remove, refresh, orientation. The only mild overlap is docs_home vs list_doc_sources, since both touch registered sources, but docs_home is orientation/guidance while list_doc_sources returns actual data.

    Naming Consistency4/5

    Most tools follow a clear verb_noun pattern (list_doc_sources, search_docs, fetch_doc, add_doc_source, remove_doc_source, refresh_doc_source). docs_home breaks the pattern with a noun-only name, a minor deviation in an otherwise consistent set.

    Tool Count5/5

    Seven tools is well-scoped for a documentation search server. Each tool earns its place: core search/fetch plus full source lifecycle management and an orientation entry point.

    Completeness5/5

    The surface covers the full lifecycle: orientation, listing sources, searching, fetching, and add/remove/refresh of sources with persisted indexing. No obvious gaps for a doc-search domain.

    Maintenance

    ActivityMaintained
    ResponsivenessResponsive