A
licenseNot graded
qualityF
maintenanceEnables AI assistants to enhance their responses with relevant documentation through a semantic vector search, offering tools for managing and processing documentation efficiently.
6 npm
64
MIT
RAG Documentation MCP Server
by jumasheff
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TDQS
A4.3/5.0
Scored across 7 tools
Disambiguation5/5
Each tool has a clearly distinct purpose with no overlap: clear_queue manages queue state, extract_urls discovers links, list_queue and list_sources provide status views, remove_documentation deletes sources, run_queue processes the queue, and search_documentation queries content. The descriptions reinforce these boundaries, making misselection unlikely.
Naming Consistency5/5
All tool names follow a consistent verb_noun pattern using snake_case, such as clear_queue, extract_urls, list_queue, list_sources, remove_documentation, run_queue, and search_documentation. This uniformity makes the set predictable and easy for agents to navigate.
Tool Count5/5
With 7 tools, the server is well-scoped for its RAG documentation purpose, covering queue management, source discovery, processing, and search. Each tool earns its place without bloat, aligning with typical server sizes of 3-15 tools for focused functionality.
Completeness4/5
The tool set covers core RAG documentation workflows including queue management, source extraction, indexing, and search, with no dead ends. A minor gap exists in lacking tools for updating or modifying existing documentation sources, but agents can work around this by re-adding URLs.
Maintenance
ActivityInactive
ResponsivenessNo issues