shelfmark
Related Servers
Alternatives to shelfmark
No user-submitted related servers found.
Related Servers
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and MCP hosts to query locally indexed notes, docs, chat logs, and other files via hybrid vector/BM25 retrieval with section-level citations. Exposes tools, resources, and prompts for searching and asking over the indexed content.-
- FlicenseNot gradedqualityAmaintenanceEnables AI hosts and agents to query locally indexed notes, docs, PDFs, chat logs, and other scattered files through hybrid vector and BM25 retrieval with section-level citations. It exposes tools, resources, and prompts for searching, asking questions, scoping by tags, traversing links, and reranking results.-
- FlicenseNot gradedqualityAmaintenanceEnables AI hosts and agents to search, ask, and retrieve cited sections from a locally indexed collection of notes, documents, PDFs, web pages, org files, and chat logs using hybrid vector and BM25 retrieval.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents to search and question a personal knowledge base of notes and documents through hybrid vector and BM25 retrieval, with section-level citations. It also lets agents store, retract, and explore durable memories, links, and indexing statistics across tools.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents to search and query a local knowledge base of scattered notes and docs through hybrid vector plus BM25 retrieval with section-level citations, and to store, retract and explore durable memories, note links and agent harness memories. It also exposes note resources plus briefing, study-plan and contradiction-check prompts, all backed by a shared local index.-
- FlicenseNot gradedqualityAmaintenanceEnables AI agents and MCP-compatible hosts to search, ask, and retrieve section-level citations from scattered local notes, docs, PDFs, Word files, web pages, org files, and chat logs using hybrid vector and BM25 retrieval.-
TDQS
Scored across 2 tools
The two tools have clearly distinct scopes: rag_query searches broadly across code, docs, and commits, while search_knowledge targets only durable knowledge artifacts. Their descriptions explicitly state when to use each, eliminating ambiguity.
Both names use lowercase snake_case and are descriptive, but the pattern differs: 'rag_query' places the technology prefix before the verb, while 'search_knowledge' follows a verb-noun structure. This minor inconsistency doesn't cause confusion, but a unified pattern like 'search_all' and 'search_knowledge' would be cleaner.
With only two tools, the server feels minimally scoped, but for a focused search/retrieval service this is arguably sufficient. The two tools complement each other well without redundancy, though a few more specialized search options could justify a higher score.
The two tools cover broad and knowledge-specific search, including source code, commits, notes, and decisions. Minor gaps exist such as lacking a tool to retrieve a specific document by ID or list available sources, but core retrieval needs are met.