Single tool 'my_tool' has critical definition gaps. Input schema visible but severely incomplete: parameter descriptions are generic Pydantic model references, not actionable LLM guidance. Tool description is absent (inferred from code only). No output schema documented. Parameter 'a' (str, required) and 'b' (int, default 0) lack individual descriptions explaining their purpose, constraints, or valid ranges. No error handling guidance. Tool naming is generic and does not follow verb_noun pattern. Code shows infrastructure for multi-protocol exposure but tool definitions themselves are underdeveloped.

• Rename 'my_tool' to a verb_noun pattern reflecting its actual action, e.g., 'process_data', 'validate_input', 'transform_record'. This enables LLMs to infer intent from the name alone.
• Add a 50 - 200 character description to 'my_tool' explaining: (1) what it does, (2) when to call it instead of similar tools, (3) what it returns. Example: 'Validates and processes user input data. Call this to normalize and check required fields before storing. Returns processed record with validation status.'
• Add individual descriptions to parameters 'a' and 'b'. For 'a': 'Required string identifier or name (1 - 255 chars, alphanumeric + underscore).' For 'b': 'Optional integer threshold (0 - 1000, default 0). Set to 0 to disable filtering.'
• Document the output schema with field names, types, and descriptions. Example: '{"status": "success|error", "result": {"id": string, "processed_at": ISO8601 timestamp}, "error_message": string (if status=error)}'
• Add error handling that returns structured errors with recovery hints. Example: 'Invalid parameter a: must be 1 - 255 chars. Got "" (empty string). Provide a non-empty identifier.' instead of a generic 500 error.