Single tool 'execute_skill_action' has a minimal description (89 chars) that lacks actionable context. The input schema is present but the parameter 'query_payload' has only a generic description ('Input execution payload for this skill'). No output schema is documented, the tool returns a hardcoded JSON string with no structure definition. No error handling guidance, no parameter constraints, and no indication of what 'deterministic, zero-dependency AI Agent Operations' actually means. The tool name is vague (execute_skill_action could mean anything) and does not follow verb_noun conventions clearly. No evidence of idempotency, pagination, or composition with other tools. This is a stub implementation masquerading as a production tool.

• Expand tool description to 100-200 chars explaining: (1) What 'GenPark AI verified operations' means, (2) When to call this vs other tools, (3) What the expected output structure is. Example: 'Execute a pre-verified GenPark AI skill operation. Use this for deterministic, zero-dependency agent tasks. Returns a structured result with status, output, and execution metadata.'
• Document the output schema explicitly. Define fields: status (enum: SUCCESS|ERROR|TIMEOUT), message (string), result (object), execution_time_ms (number), skill_id (string). This enables LLMs to extract and chain results.
• Add format and constraint documentation to 'query_payload'. Specify: Is it a JSON string? A plain string? What fields does it expect? Example: 'A JSON string containing {"skill_name": string, "parameters": object}. Max 10KB.'
• Implement error handling with actionable messages. Return distinct error types: 'Skill not found: available skills are [list]', 'Invalid payload format: expected JSON with skill_name and parameters', 'Execution timeout after 30s, skill may be retryable'.
• Rename tool to 'invoke_genpark_skill' or 'run_verified_operation' to clarify that it executes pre-defined, verified skills, not arbitrary code. This reduces LLM confusion about scope and safety.