Single tool 'execute_skill_action' has a minimal description (56 chars) that lacks actionable context for LLM selection. Input schema is present with two parameters (issue_ticket_id, modified_files) but both lack descriptions entirely, violating the critical rule that every parameter needs a description. No output schema is documented. The tool name uses 'execute' (generic verb) rather than a specific action verb like 'analyze_impact' or 'assess_code_impact'. No error handling guidance, no recovery paths, and no indication of what the tool returns or when to use it. The implementation in mcp_server.py is a stub that only prints a JSON status message, it does not actually implement the MCP protocol (no request/response handling, no tool invocation logic). This is a non-functional skeleton.

• Expand tool description to 100-200 chars explaining: 'Analyzes the impact of a code change on downstream services by mapping the issue ticket to modified files and computing a context graph. Returns affected services, risk level, and node count. Use this to assess blast radius before deployment.'
• Add descriptions to both input parameters: issue_ticket_id: 'The issue ticket identifier (e.g., ENG-4091). Used to correlate code changes with tracked work items.' modified_files: 'Array of file paths modified in the code change (e.g., ["src/auth/jwt.ts", "src/auth/middleware.ts"]). Used to trace impact through the codebase.'
• Document the output schema with field descriptions: affected_services (array of service names), downstream_risk_level (enum: LOW_ISOLATED, MEDIUM_CONTAINED, HIGH_WIDESPREAD), context_graph_nodes (integer count of mapped nodes).
• Rename tool from 'execute_skill_action' to 'analyze_code_impact' or 'assess_issue_impact' to use a specific action verb that clearly conveys intent.
• Implement actual MCP protocol in mcp_server.py: parse JSON-RPC requests from stdin, invoke the skill logic, return structured responses with proper error handling and recovery guidance.