This server has a single tool 'execute_skill_action' with virtually no definition. The tool description is present but extremely vague (28 chars: 'Execute the conversational candidate screening interview skill action'). No input schema is visible in the source code, the tool accepts no documented parameters, making it impossible for an LLM to understand what inputs it expects or how to invoke it. The mcp_server.py file contains only a stub that prints a JSON status message and does not implement the MCP protocol at all. There is no actual tool registration, no parameter definitions, no output schema documentation, and no error handling. The client.py and example_usage.py files show a Python class with a hardcoded evaluate_candidate() method that returns mock data, but this is not wired into the MCP server. The server does not conform to the MCP specification.

• Define a complete input schema for execute_skill_action with required parameters: candidate_resume_text (string, required), job_requirements (object, optional). Add descriptions for each parameter explaining format and constraints.
• Expand tool description to 100 - 200 characters: 'Evaluate a candidate's qualifications against job requirements using conversational screening. Returns competency score, interview summary, and advancement recommendation. Requires resume text and optional job role/requirements.'
• Document the output schema with typed fields: competency_score_pct (number, 0 - 100), interview_transcript_summary (string), recommendation (enum: ADVANCE_TO_TECHNICAL_ROUND | ADVANCE_TO_BEHAVIORAL_ROUND | REJECT | HOLD_FOR_REQS_CLARIFICATION).
• Implement the MCP protocol in mcp_server.py: handle initialize requests, register the tool with schema, implement the tools/call handler to invoke the skill logic, and return structured responses.
• Rename tool to 'screen_candidate' or 'evaluate_candidate' to clearly signal the action and follow verb_noun naming convention.