Document RAG MCP
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- FlicenseNot gradedqualityCmaintenanceEnables document Q&A and knowledge retrieval through hybrid semantic and keyword search, with tools for document ingestion, chunking, summarization, PII redaction, and RAGAS-based evaluation.-
- AlicenseBqualityAmaintenanceEnables file-based knowledge management with ranked keyword and semantic hybrid search, allowing AI agents to learn from documents and recall relevant knowledge as a persistent memory tool.30126 npmAGPL 3.0
- AlicenseNot gradedqualityAmaintenanceEnables agents to build self-hosted knowledge bases from document folders and search them via hybrid vector and keyword retrieval, with eight tools for listing, adding, deleting, and reindexing content.258 npm1MIT

Infino MCP serverofficial
AlicenseAqualityAmaintenanceEnables AI agents to perform keyword, semantic, hybrid, and SQL retrieval over data stored on object storage or local paths, using a local embedding model without requiring an API key.15125 npmApache 2.0- AlicenseNot gradedqualityCmaintenanceProvides semantic code search and retrieval capabilities for AI agents, enabling them to query codebases using natural language with automatic learning, hybrid search, and intelligent chunking of functions and classes.10 npm30ISC
- 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
TDQS
Scored across 12 tools
Most tools target distinct resources or actions, but query_tables and aggregate overlap for table analytics, and get_index_status substantially overlaps with health. Descriptions help clarify intent, but an agent could occasionally select the wrong one.
Tool names mostly follow a verb_noun snake_case pattern like list_tables, index_document, and get_config. The main inconsistency is the bare noun 'health' instead of something like 'get_health', and the mix of query/search/aggregate verbs is acceptable but slightly varied.
Twelve tools is well-scoped for a document RAG server covering indexing, retrieval, table querying, and operational status. Each tool serves a credible purpose without feeling bloated or thin.
Core workflows are covered: indexing, directory indexing, search, document retrieval, table listing/querying, and status checks. The main gap is the lack of a delete/remove tool for indexed documents or tables, though force re-indexing mitigates updates.