This server has critical definition failures. The single tool 'execute_skill_action' has no visible input schema, no parameter descriptions, and a vague 18-character description. The mcp_server.py file contains only a stub that prints JSON and exits, it does not implement the MCP protocol at all. No tool registration, no request/response handling, no schema validation. The client.py and example_usage.py files are standalone Python classes unrelated to MCP. This is not a functional MCP server.

• Add a complete input schema to execute_skill_action with typed parameters (e.g., goal: string, agents: array, timeout: integer). Every parameter must have a type and description.
• Rewrite the tool description to 50 - 200 characters, answering: What does it do? When should the LLM call it? What does it return? Example: 'Decompose a high-level goal into subtasks and delegate them to available agents. Returns an execution plan with step-by-step tasks, assigned agents, and estimated duration.'
• Add parameter descriptions for all inputs. Example: 'goal (string, required): The high-level objective to decompose. agents (array of strings, optional): List of available agent names (e.g., ["ResearchAgent", "CoderAgent"]). If omitted, uses default agents.'
• Implement the MCP protocol in mcp_server.py: add a request/response loop that handles tool calls, validates inputs against the schema, and returns structured results. Use the official MCP Python SDK or reference implementation.
• Document the output schema. Example: 'Returns {execution_graph_steps: [{step: int, task: string, agent: string}], estimated_duration_seconds: int, parallel_executable: bool}'. Include field descriptions.