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' with a generic string type and vague description ('Input execution payload'). No output schema is documented. The tool name uses a verb but is overly generic ('execute_skill_action' could mean anything). No error handling guidance, no parameter constraints, no examples of valid payloads. The implementation in mcp_server.py returns a hardcoded success response regardless of input, indicating the tool is non-functional or a stub. This server appears to be a proof-of-concept rather than a production-ready MCP implementation.

• Expand tool description to 100-200 characters explaining: (1) What the tool does (e.g., 'Executes a GenPark AI skill operation'), (2) When to use it (e.g., 'Use this to run deterministic agent tasks'), (3) What it returns (e.g., 'Returns a result object with status and output fields'). Reference pattern:tool-description.
• Add format and validation constraints to 'query_payload': specify expected structure (JSON object? string? specific fields?), provide an example valid payload, and document any required or optional sub-fields. Use enum or pattern constraints if applicable.
• Document the output schema explicitly in the tool definition. Include field names, types, and descriptions for all response fields (e.g., 'status: string (SUCCESS|ERROR|PENDING)', 'message: string (human-readable result)', 'data: object (operation-specific output)').
• Implement actual tool logic instead of returning hardcoded responses. The tool should parse query_payload, validate it, execute the skill, and return real results or meaningful errors.
• Add error handling that returns structured error responses with: (1) error code, (2) human-readable message, (3) classification (retryable/user-fixable/fatal), (4) recovery suggestion (e.g., 'Retry with a valid payload' or 'Check GenPark documentation for supported operations').