Single tool 'execute_skill_action' has a minimal description (97 chars) that lacks actionable context for LLM selection. Input schema is present but parameter 'query_payload' has only a generic description ('Input execution payload for this skill') with no format, constraints, or examples. No output schema is documented, the tool returns a hardcoded JSON string with no field descriptions. No error handling guidance, no idempotency markers, and the tool name does not follow verb_noun convention (should be 'execute_action' or similar). The server lacks composition clarity: it is unclear what 'Long-Horizon Coding & Memory operations' means or when an agent should invoke this tool vs. alternatives. No parameter validation rules, no recovery guidance on failure.

• Rename 'execute_skill_action' to a concrete verb_noun pair that reflects the actual operation. Based on client.py, consider 'record_collision_hypothesis' or 'index_failed_patch'.
• Add format and constraint rules to 'query_payload': specify expected JSON structure, required fields, length limits, and provide a concrete example payload.
• Document the full output schema with field descriptions: what does 'status' mean? What are valid values? What does 'message' contain? Include example responses.
• Expand tool description to 80-150 chars explaining: (1) what operation it performs, (2) when to call it (e.g., 'after a patch attempt fails'), (3) what it returns (e.g., 'collision detection result and recommended strategy').
• Add error handling: define failure modes (invalid payload, timeout, service unavailable), classify each as retryable/fatal, and provide recovery guidance (e.g., 'If timeout, retry with smaller payload').