F
licenseNot graded
qualityB
maintenanceEnables agents to search a document collection by query, fetch a complete document by its ID, and generate bounded summaries of supplied text, with Pydantic-validated tool inputs.
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rag-mcp
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TDQS
A4.4/5.0
Scored across 5 tools
Disambiguation5/5
Each tool has a clearly distinct role: retrieval search, chunk-context reading, whole-document reading, index diagnostics, and LLM answer generation. The descriptions explicitly contrast rag_search with rag_answer and rag_read_around with rag_read_document, leaving little room for misselection.
Naming Consistency4/5
All names use a consistent snake_case rag_ prefix, and most follow a verb or verb-noun pattern such as rag_search and rag_read_document. rag_index_stats is slightly noun-oriented rather than action-oriented, but it remains readable and predictable.
Tool Count5/5
Five tools is well-scoped for a RAG retrieval server, with each tool covering a distinct part of the retrieval-and-answer workflow. There are no redundant tools and no obviously missing core operation within this small surface.
Completeness4/5
The set covers search, contextual reading, full-document reading, diagnostics, and generated answers, which are the core retrieval workflows. Minor gaps might include listing indexed documents or collections directly, but rag_index_stats and search largely compensate.
Maintenance
ActivityMaintained
ResponsivenessNo issues