QLog MCP demonstrates strong schema completeness and parameter documentation across 9 tools. All tools have descriptions (avg 180 chars, within 10-1024 baseline) and detailed input schemas with types, enums, and constraints. Tool naming follows verb_noun convention (get_, query, match_, aggregate, compare_). However, output schemas are documented only via Annotated Field descriptions rather than explicit response type schemas in the tool definitions. Error handling is minimal, no recovery guidance or actionable error messages visible in the code. Tool composition is excellent: each tool has a single responsibility, and the server instructions guide LLM sequencing. Security is sound: all tools are read-only with proper annotations. Minor gaps: descriptions could be more concise (some exceed 200 chars), and no explicit error classification or retry guidance.

• Add explicit response type schemas to all tools. Define a Pydantic model for each tool's return value and document it in the tool registration (e.g., @server.tool(..., output_schema=GetContextResponse)).
• Implement error handling with actionable messages. Wrap tool logic in try-catch blocks that return structured errors: {"error": "<category>", "message": "<actionable detail>", "suggestion": "<recovery step>"}.
• Reduce parameter description length to 50-150 chars. Move detailed semantics to the server instructions (already excellent) and keep param descriptions concise for LLM parsing.
• Rename 'membership.match_qso' to 'membership.match_roster' to disambiguate from 'catalog.match_qso' and clarify that it operates on roster data, not catalog data.
• Add pagination guidance to tool descriptions: 'Results are paginated; use limit (1-1000) and offset to retrieve large datasets. Default limit is 100.'

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry