AI Knowledge Base MCP Server
Related Servers
Alternatives to AI Knowledge Base MCP Server
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityBmaintenanceProvides read-only access to a personal RAG knowledge base, enabling hybrid search, evidence-grounded retrieval with citations, and knowledge gap tracking for LLM agents.-
- AlicenseAqualityCmaintenanceEnables local-first hybrid knowledge retrieval from authorized Markdown and plain-text files, combining full-text and vector search with reranking and traceable source references via a single search tool.1MIT
- FlicenseNot gradedqualityCmaintenanceEnables agents to search a local, fully offline personal document library (Markdown/txt/PDF/docx) via SQLite FTS5 trigram search with subject-terminology expansion, returning every hit with its domain, document title, section path, and page or offset provenance. It also exposes domain statistics, incremental ingestion, and document deprecation as tools, alongside a read-only, runtime-persona-scoped ask/coverage mode.-
- AlicenseNot gradedqualityBmaintenanceProvides read-only search and context-pack creation over a local source library, letting AI assistants retrieve relevant excerpts and audit cited quotations.MIT
- AlicenseAqualityAmaintenanceEnables coding agents to query local notes, decisions, docs, and code with hybrid retrieval (BM25 + embeddings + reranking) and get path:line citations. It provides tools like rag_query for full-corpus search and search_knowledge for project-scoped knowledge recall.21MIT
- FlicenseAqualityDmaintenanceEnables AI agents to query a local knowledge graph built from document collections using hybrid search (BM25 + vector fusion) and entity-relationship extraction. Supports privacy-first, offline operation with tools for semantic search, entity graph exploration, and corpus statistics.3-
TDQS
Scored across 4 tools
get_context, search, and discover all relate to retrieving information, which creates some overlap; however, search is query-based, discover is browse-based, and get_context aims for comprehensive retrieval, so descriptions mostly disambiguate. get_status is clearly separate. The boundary between get_context and search remains slightly fuzzy.
Two tools use the get_ prefix while two use bare verbs, creating a mixed convention. The names are still readable and each clearly indicates its action, but the inconsistent style is noticeable.
Four tools is a reasonable size for a focused knowledge-base retrieval server, though it feels on the lean side. Each tool has a distinct role and the count is appropriate for the apparent scope.
The server only provides retrieval and status operations; there are no tools for adding, updating, or deleting knowledge-base content. For a server named 'AI Knowledge Base', this is a significant gap that limits agents to read-only workflows.