Severe quality gaps across all dimensions. Only 1 of 4 tools has a visible, complete definition (get_weather in mcp_server.py). The other 3 tools (run-aws-code, list-credentials, select-profile) are defined in pytonmcp_server.py but lack explicit tool registration, tool names, descriptions, and schemas are inferred from Pydantic models and comments, not from actual @mcp.tool() decorators or equivalent registration. get_weather has a minimal description ('Returns weather information for a given city') and no documented output schema. Parameters across all tools lack descriptions. Error handling is absent. Security is critically compromised: run-aws-code accepts arbitrary JavaScript code execution and AWS credentials as parameters, violating secret-injection and command-injection patterns.

• Add explicit @mcp.tool() decorators with full schemas to run-aws-code, list-credentials, and select-profile in pytonmcp_server.py. Currently they are inferred from Pydantic models, not registered.
• Remove 'code' and credential parameters from run-aws-code. Instead, inject AWS credentials server-side via environment variables or boto3 session. Never expose code execution or secrets as tool parameters.
• Add parameter descriptions to all tools. Example: 'city (string): The name of the city to fetch weather for (e.g., London, New York). Required.' Baseline: 72 chars per param.
• Document output schemas for all tools. Example for get_weather: 'Returns {weather: string} with current conditions, or {error: string} on failure.'
• Add error handling guidance. Example: 'If city not found, returns {error: "City not found. Try a major city name."}' to guide LLM recovery.

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

• Relies on Dynamic Client Registration (deprecated) - use Client ID Metadata Documents (CIMD)