lemma_docs_mcp
Related Servers
Alternatives to lemma_docs_mcp
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityCmaintenanceProvides read-only, citation-backed semantic search and retrieval-augmented generation over enterprise documents via standardized MCP tools, with local embeddings for privacy.-
- FlicenseNot gradedqualityCmaintenanceProvides read-only MCP tools for hybrid semantic and keyword search over locally indexed PDF documentation, with citations and context retrieval for LLM agents.-
- 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-
- AlicenseNot gradedqualityBmaintenanceEnables semantic search over a local knowledge base using MCP tools, allowing AI clients to retrieve relevant document chunks via the search_knowledge tool.340 npm2MIT
- AlicenseNot gradedqualityBmaintenanceEnables any MCP-capable LLM client to search self-hosted long-term memory over markdown and PDF documents, combining dense semantic vectors with BM25 keyword retrieval and optional cross-encoder reranking. Exposes a read-only tool surface for querying incidents, runbooks, and other knowledge-base content, while writes happen out-of-band through ingestion jobs or a token-gated internal API.1MIT
- AlicenseNot gradedqualityBmaintenanceEnables AI tools to securely search and retrieve relevant, source-attributed chunks from private local documents via MCP, without sending document content to third-party services.1MIT
TDQS
Scored across 2 tools
The two tools have clearly distinct roles: one performs ranked search/discovery over the corpus, the other retrieves full text for specific chunks or documents. There is no overlap or ambiguity between them.
Both tool names follow the same `lemma_docs_<verb>` pattern, with context indicating search/discovery and get indicating retrieval. The naming convention is consistent and predictable.
Two tools is on the thin side, but for a narrow local-documentation retrieval server the search-and-get pair is a reasonable minimal setup. It feels slightly sparse rather than fully fleshed out.
The server covers the core documentation workflow: discover relevant chunks and then expand them to full text. Notable gaps like listing all documents or browsing the corpus are acknowledged by the tool descriptions, and the search tool can surface paths, so agents can work around them.