Five tools with clear action verbs and reasonable descriptions (avg 156 chars), but significant gaps in schema documentation and parameter descriptions. All tools are READ_ONLY, reducing risk. However, input schemas are not visible in the provided source code, only parameter names and types are inferred from the description text. gauntlet_run and gauntlet_respond have the most complete parameter documentation; gauntlet_results and gauntlet_leaderboard have minimal input specs. No output schemas documented. Error handling and recovery guidance absent.

• Add formal JSON Schema definitions for all tool inputs. Declare parameter types (string, boolean, etc.), required fields, enums (e.g., tier: ['gpu', 'cpu', 'apple_silicon']), and constraints (e.g., session_id: minLength 1).
• Document output schemas for all tools. Specify what fields each tool returns (e.g., gauntlet_run returns {session_id, prompt, ...}; gauntlet_results returns {model_id, scores, ...}). Include field types and descriptions.
• Expand descriptions for gauntlet_status and gauntlet_results to 100+ chars. Explain when to call each tool, what data they provide, and how they relate to the benchmark workflow.
• Add error handling guidance to tool descriptions. E.g., 'If session_id is invalid, returns error 404. Call gauntlet_run() to start a new session.' This guides LLM recovery.
• Declare gauntlet_leaderboard 'tier' as an enum with explicit valid values, or document the constraint in the parameter description with a regex pattern.

Inferred effective spec: 2026-07-28+.

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