docsearch-mcp
Related Servers
Alternatives to docsearch-mcp
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and MCP hosts to search and query a local hybrid vector/BM25 index of scattered notes, docs, PDFs, Office files, web pages, org files, and chat exports. It returns section-level citations so agents can ground answers in the user's own files.-
- AlicenseNot gradedqualityCmaintenanceEnables AI clients to search and read a folder of Markdown/text documents via a read-only MCP server with hybrid BM25 and local embedding search, treating the calling model as untrusted.11 npmMIT
- AlicenseNot gradedqualityAmaintenanceEnables AI agents to search local text and Markdown collections using keyword BM25, semantic vector search, and reranked hybrid retrieval, with full-document retrieval and collection status over MCP.1MIT
- FlicenseNot gradedqualityBmaintenanceEnables fast local hybrid search over a folder of markdown files via MCP, combining BM25 and vector similarity with explicit relevance verdicts and no LLM or network calls at query time.1-
- AlicenseAqualityAmaintenanceSemantic code search for any MCP client: indexes local folders of code and docs and answers plain-language or code queries with matching passages, file paths, line ranges and scores. Embedding runs on the local CPU with the Motherlode code-search encoder, so no file content leaves the machine; the encoder downloads once from Hugging Face. Install: npx -y blackwindow-weave-mcp.6430 npmBusiness Source 1.1
- FlicenseNot gradedqualityBmaintenanceEnables any MCP-compatible AI assistant to search, filter, and retrieve information from a local document collection using a hybrid search pipeline with vector, BM25, reranking, and LLM enrichment.4-
TDQS
Scored across 2 tools
The two tools have clearly distinct purposes: one searches content by relevance, the other enumerates indexed sources. There is no realistic way to confuse a BM25 search with listing sources.
Both names follow the same verb_noun snake_case pattern (search_docs, list_sources). The convention is predictable and readable.
Two tools is thin for a search server; useful capabilities like fetching a full document or filtering search by source are absent. Still, the count is not absurd for a narrowly scoped read-only index.
Core retrieval and source enumeration are covered, but there is no way to retrieve a whole document, filter searches by source, or manage the index. Agents hitting a chunk boundary or needing document-level context will dead-end.