A Model Context Protocol (MCP) server that provides RAG capabilities to Cursor using Qdrant vector database for semantic search and document retrieval
Scoring was not performed
Output schemas not documented for any tool. LLMs cannot plan downstream operations or extract needed fields without knowing return structure.
Parameter descriptions lack validation constraints. 'limit' and 'offset' have no min/max bounds, 'num_clusters' has no range, 'expand_level' has no definition of valid depth levels. This invites absurd values (limit=999999, num_clusters=-5).
No pagination guidance for list-returning tools. 'search' accepts limit/offset but no documentation on whether results are capped, whether a cursor is returned, or what the max limit should be. Large result sets will bloat context windows.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 43 | 2026-07-28+ | v2 |
| 2026-03-09 | C | 67 | 2025-06-18+ | v1 |
Tool descriptions are too brief and lack operational guidance. 'hierarchy_search' (42 chars) does not explain when to use it instead of 'search', what 'hierarchy awareness' means, or what data structure is returned. Minimal descriptions force LLMs to guess intent.
No error handling or recovery guidance documented. Tools provide no indication of when they fail, what errors are retryable, or what recovery steps the agent should take.
Parameter 'filters' in 'search' accepts object type but does not document valid filter fields, operators, or syntax. This is too vague for reliable LLM use, LLMs cannot infer the filter DSL without explicit examples or formal spec.
Tool names like 'analyze_relationships', 'detect_document_conflicts', 'find_complementary_content' are somewhat vague. 'analyze_relationships' could mean find, measure, rank, or visualize relationships, the description does not clarify. Better: 'list_document_relationships' or 'rank_document_relationships'.
No documentation of what fields are returned in responses or what IDs/references are available for chaining. If 'search' returns documents, does it include document_id, cluster_id, and other downstream tool IDs? Without this, LLMs cannot chain tools efficiently.