document-index-mcp
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- AlicenseNot gradedqualityAmaintenanceTurns a local folder of documents into a queryable index that an LLM agent can pull from on demand, keeping only a compact table of contents in context. Exposes five tools (index, search, read, grep, neighbours) over a single offline SQLite FTS5 store so the model retrieves exact, citable sections instead of being handed whole files.MIT
- FlicenseAqualityDmaintenanceEnables indexing local documents (PDF, Markdown, text, code) into a knowledge base and querying them via semantic search using local embeddings, all running privately on your machine.4-
- AlicenseAqualityAmaintenancePrivacy-first local document search using semantic search. Runs entirely on your machine with no cloud services, supporting PDF, DOCX, TXT, and Markdown files.2295,718 npm403MIT
- AlicenseNot gradedqualityAmaintenanceLocal-first memory and retrieval for private project knowledge. Enables indexing files, searching, and asking questions about project documents using local embeddings and LLM.6AGPL 3.0
- AlicenseNot gradedqualityAmaintenanceLocal-first document management and semantic search for AI coding agents, with a built-in web dashboard and REST API.96 npm344MIT
- AlicenseAqualityAmaintenanceThe open retrieval layer for AI agents. Index your entire project — code, docs, legal, research, data — and serve surgical context via MCP. FTS5 full-text search, optional semantic search (FastEmbed/ONNX), 10 built-in parsers, incremental auto-sync.8574 PyPI24MIT
TDQS
Scored across 5 tools
Each tool has a distinct role: search_document finds snippets, get_chunk_context retrieves full text, get_document_outline provides structure, ingest_document adds content, and delete_document removes it. There is no overlap in purpose; the descriptions clearly differentiate when to use each.
All tool names follow a consistent verb_noun pattern (ingest_document, delete_document, search_document, get_chunk_context, get_document_outline). The prefix 'get_' is used consistently for retrieval operations, and the other verbs are clear and action-oriented.
Five tools is well-scoped for a document indexing and retrieval server. Each tool is necessary and there are no redundant or superfluous entries. This is within the ideal range for a focused MCP server.
The tool surface covers the full document lifecycle: ingest (create), read via search/context/outline, update via re-ingestion (explicitly stated), and delete. It also includes library listing and ingestion status polling, so there are no obvious dead ends or missing capabilities for the stated purpose.