The server defines 6 tools with explicit FastMCP registration and clear action-verb naming. All tools have descriptions and documented parameters, placing this above the median. However, several critical gaps prevent a higher score: (1) parameter descriptions lack constraint information (ranges, enums, format requirements), (2) output schemas are not documented, tools return JSON strings but LLMs cannot see the structure, (3) error handling is minimal with no recovery guidance, (4) some descriptions are generic and could better explain WHY to use a tool vs alternatives. The execute_code tool is the strongest (good usage guidance), but others (get_health_status, get_pool_stats) are minimal. Average param description length is ~40-60 chars, below the 72-char baseline for A+ tools.

• Add formal output schema documentation for each tool. E.g., execute_code returns {"status": "success"|"error"|"timeout", "output": string, "error_type": string, "session_id": string}. Document whether errors are always status=error or an exception, and what fields vary.
• Add enum constraint for 'language' parameter: ["python", "javascript", "java", "cpp", "go", "r"]. Move this from description text into the schema so LLMs and validators can enforce it.
• Expand parameter descriptions with ranges and formats: 'timeout: Integer, 1 - 3600 seconds (default 30). Requests exceeding this duration are terminated and return a timeout error.' Similar guidance for all constrained parameters.
• Add recovery guidance to error responses. Instead of returning '{"error": "Connection refused"}', return '{"error": "Connection refused", "recovery": "The code execution backend is unavailable. Try get_health_status() to check service status, or retry in 30 seconds."}' (requires API change or wrapper).
• Document session lifecycle in execute_code description: 'Reuse session_id from create_session() across multiple code calls to maintain state. Pass a new session_id or omit it to create an implicit session. Use close_session() to free resources when done.'
• Expand get_health_status and get_pool_stats descriptions to explain what metrics they return and why an LLM would want to call them. E.g., 'Call before large batch jobs to ensure capacity; inspect pool_stats.available_sessions to decide if queuing is needed.'

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

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