This server is non-functional as an MCP implementation. The mcp_server.py file does not implement the MCP protocol at all, it merely prints a JSON status message and exits. There is no request/response handling, no tool invocation mechanism, and no actual integration with the single declared tool 'execute_skill_action'. The tool has no visible schema, no parameter definitions, and no error handling. The client.py and example_usage.py files are standalone Python classes unrelated to MCP. This is a stub, not a working server.

• Implement a complete MCP server that listens on STDIO (or preferably HTTP/SSE), parses CallTool requests, invokes execute_skill_action with proper parameter validation, and returns structured results.
• Define execute_skill_action with a full JSON Schema input specification: list all required and optional parameters, their types (string, number, enum, etc.), descriptions, and constraints. Example: { 'skill_name': { 'type': 'string', 'description': 'Name of the skill to execute (e.g., sql_query, web_search)' }, 'parameters': { 'type': 'object', 'description': 'Skill-specific parameters as key-value pairs' } }.
• Expand the tool description to 100 - 200 characters explaining: (1) what skill actions are, (2) when to call this tool vs. alternatives, (3) what the return value contains, (4) whether it modifies state. Example: 'Execute a named skill action for agent task evaluation. Provide the skill name and parameters. Returns evaluation results including pass/fail status and detailed metrics. Use this to run benchmark scenarios.'
• Add comprehensive error handling: return actionable error messages (e.g., 'Skill not found: sql_query. Available skills: [web_search, sql_query, code_execute]') and classify errors as retryable or fatal.
• Document the output schema: what fields does the result contain? What are their types? Example: { 'success': boolean, 'skill_name': string, 'execution_time_ms': number, 'result': object, 'error': string (if failed) }.