Single tool 'execute_skill_action' has a minimal description (89 chars) that lacks actionable context. The input schema is present but extremely sparse: only one parameter 'query_payload' (string) with a generic description. No output schema is documented, the tool returns a hardcoded JSON response with 'status' and 'message' fields, but this structure is not declared in the tool definition. No error handling guidance, no parameter constraints, no indication of what 'query_payload' should contain or how it's processed. The tool name does not follow verb_noun convention (should be something like 'execute_agent_action' or 'run_skill'). The implementation is a mock that always returns success, providing no real validation or error scenarios.

• Add a detailed output schema to the tool definition documenting the response structure: {type: 'object', properties: {status: {type: 'string', enum: ['SUCCESS', 'FAILURE', ...]}, message: {type: 'string'}, ...}, required: [...]}}
• Expand tool description to 100-200 characters explaining: what GenPark AI verification means, what types of operations are supported, and when to call this tool vs. other agent tools.
• Add constraints to 'query_payload' parameter: specify expected format (JSON string? plain text?), maximum length, and provide a concrete example of valid input.
• Rename tool to follow verb_noun convention: 'run_genpark_skill' or 'execute_agent_operation' to make intent clear to LLMs.
• Document error scenarios: what happens if query_payload is malformed, if GenPark service is unavailable, or if verification fails. Include recovery guidance for each error type.