MCP Elasticsearch Demo
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- 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
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- AlicenseAqualityAmaintenanceEnables local AI agents to perform hybrid search (BM25 + vector) over privately indexed local files, including notes, PDFs, DOCX, source code, and OCR'd images. All indexing and retrieval stay on the machine with read-only, whitelist-scoped access.3MIT
TDQS
Scored across 5 tools
Each tool has a distinct retrieval purpose: search_documents queries by text, find_similar queries by example id, list_facets enumerates filter values, get_document fetches a full doc by id, and get_video_segment pulls a time-bounded transcript. The main mild overlap is search_documents vs find_similar (both return ranked recommendations), but the input mode (query vs id) and descriptions differentiate them adequately.
All five names follow a consistent snake_case verb_noun pattern (get_video_segment, search_documents, get_document, list_facets, find_similar). Verb choice is predictable and readable across the set.
Five tools is well-scoped for a hybrid search/retrieval server, covering search, drill-down, filtering discovery, similarity, and a media-specific accessor. Every tool earns its place with no redundant filler.
The retrieval lifecycle is well covered: search, fetch full content, discover facets, find similar, and extract a video segment. It lacks any indexing/write operations (create/update/delete or list-all), which may be intentional for a read-only demo but leaves the surface one-sided.