The server exposes only 1 tool ('retrieve') with a READ_ONLY risk classification. The tool definition exists in source but lacks depth. Tool name 'retrieve' is generic and does not follow verb_noun pattern (should be 'search_documentation' or 'retrieve_documentation'). Description is minimal (42 chars: 'Search the documentation collection and return relevant context.'), below the 50-200 char optimal range for LLM optimization. Input schema is present but sparse: only a 'query' string parameter with description. Output schema is NOT documented in the provided source code (no return type annotation visible). No error handling guidance. No pagination parameters despite the tool likely returning lists (RETRIEVE_TOP_K=5 suggests results are capped but not configurable). No parameter constraints (e.g., max query length). The tool appears to be a thin wrapper over Qdrant vector search without sufficient schema documentation for agents to understand output structure, field names, or how to chain results into downstream actions.

• Rename 'retrieve' to 'search_documentation' to follow verb_noun convention and clarify action.
• Expand description to 80-150 characters, explicitly stating: WHAT it does (searches docs), WHEN to use it (to find relevant context for user queries), output type (list of document snippets), and any limits (top 5 results, max query 500 chars).
• Document the output schema in code: define return type as List[Dict] with fields like 'content' (str), 'source' (str), 'score' (float). Include field descriptions so agents know what to extract.
• Add 'limit' parameter (optional, default=5, range 1-50) to give agents control over result count without hardcoding.
• Add validation in the implementation: reject empty queries, enforce max query length (e.g., 1000 chars), return clear error messages like 'Query cannot be empty' or 'Query exceeds 1000 character limit'.
• Include at least one example in the tool description of a good query and expected output structure, e.g., 'Example: query="How do I authenticate users?" returns list of document snippets with relevance scores.'.
• Add 'relevance_threshold' parameter (optional, default=0.5) so agents can filter low-confidence results.