This server has severe definition quality issues. Tool definitions are inferred from client code rather than explicitly registered via MCP protocol. execute_skill_action has no visible schema or description in mcp_server.py. orchestrate_agent_debate has a description and partial schema visible in client.py, but the tool is not registered as an MCP tool, it's a Python class method. The mcp_server.py only prints a JSON status message and does not implement the MCP protocol's tool registration mechanism. No input validation, error handling, or output schema documentation. Descriptions are present but lack actionable detail for LLM selection.

• Implement proper MCP protocol tool registration in mcp_server.py using the MCP SDK. Each tool must be registered with a name, description, and input schema via the tools/list and tools/call handlers.
• Add comprehensive descriptions to both tools (50 - 200 chars each). For orchestrate_agent_debate: 'Orchestrate a multi-agent debate to reach consensus on a decision. Agents assume roles (ARCHITECT, SECURITY_OFFICER, DBA, etc.) and debate a problem statement for up to N rounds. Returns consensus verdict, agreement score, and action plan. Use when you need structured multi-perspective analysis before a critical decision.'
• Document the output schema for orchestrate_agent_debate as a JSON Schema object with fields: consensus_session_id (string), rounds_completed (integer), consensus_verdict (enum), inter_agent_agreement_score_pct (number 0 - 100), resolved_action_plan_steps_count (integer), debate_transcript_log_url (string).
• Add input parameter descriptions. For participating_agent_roles, clarify: 'Array of agent role names. Valid roles: ARCHITECT, SECURITY_OFFICER, DBA, PRODUCT_MANAGER, COMPLIANCE_OFFICER. Each role brings a distinct perspective to the debate.'
• Implement error handling with actionable messages. E.g., if max_debate_rounds exceeds 10, return: 'Invalid max_debate_rounds: 15. Must be 1 - 10 to avoid excessive latency. Retrying with max_debate_rounds=10.'