Single tool 'execute_skill_action' has a minimal description (97 chars) that lacks actionable context. Input schema is present but 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 the tool actually does beyond vague claims about 'GenPark AI Deep Research.' The implementation is a stub that ignores the input and returns a static success message, making it unsuitable for production use.

• Document the output schema explicitly. Define the structure of the returned JSON object with typed fields (e.g., status: string, message: string, results: array, metadata: object). Include field descriptions.
• Rewrite the parameter description for 'query_payload' to specify format, constraints, and examples. E.g., 'A JSON string or natural language query describing the research topic. Max 2000 chars. Example: {"topic": "battery technology", "scope": "maritime"}'
• Replace the tool description with concrete, actionable text. E.g., 'Decompose a complex research query into sub-queries and generate a synthesis plan. Returns a list of focused sub-queries, estimated sources needed, and a URL to the query plan. Use this to break down multihop research tasks before executing individual searches.'
• Implement actual logic in execute_skill_action. Currently it ignores input and returns a hardcoded response. Parse query_payload, validate it, and return meaningful results or clear error messages.
• Add error handling. Return structured errors with actionable guidance: 'Invalid query_payload: expected JSON object with "topic" and "scope" fields. Example: {"topic": "battery tech", "scope": "maritime"}'