llmstxt-doc-search
Related Servers
Alternatives to llmstxt-doc-search
No user-submitted related servers found.
Related Servers
- AlicenseNot gradedqualityDmaintenanceAggregates 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.218 npmMIT
- FlicenseNot gradedqualityAmaintenanceEnables 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.-
- FlicenseNot gradedqualityAmaintenanceEnables AI models to search for and retrieve the latest technical documentation from popular agent frameworks (such as langchain, llama-index, autogen, agno, openai-agents-sdk, mcp-doc, camel-ai, and crew-ai) by running site-scoped web searches and extracting the page text. It can be run locally over stdio or deployed remotely over SSE for use with clients like Cursor and Cline.-
- AlicenseNot gradedqualityCmaintenanceEnables fast, token-efficient access to large documentation files in llms.txt format through semantic search. Solves token limit issues by searching first and retrieving only relevant sections instead of dumping entire documentation.3MIT
- FlicenseAqualityCmaintenanceGives an LLM client BM25 keyword search over a local corpus of Markdown and text files, letting it find ranked passages, fetch exact chunks with source offsets, read whole documents, and list what was indexed. It runs entirely locally over stdio with an in-memory index, calling no model and requiring no API key.4-
- AlicenseAqualityCmaintenanceEnables LLM hosts to retrieve live, relevant documentation excerpts from official library docs sites via a search-and-RAG tool, avoiding reliance on training data.1MIT
TDQS
Scored across 7 tools
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.
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.
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.
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.