This server has a single tool 'execute_skill_action' with virtually no definition. The tool name is vague (execute_skill_action could mean anything), the description is trivial (18 chars, below the 20-char minimum), and no input schema is visible in the source code. The mcp_server.py file only prints a JSON status message and does not register tools with proper MCP protocol structures. The client.py and example_usage.py files show a mock implementation that returns hardcoded NPC behavior data, but this is not an actual MCP tool definition. No parameters are documented, no output schema is defined, and no error handling exists. This is a non-functional MCP server stub.

• Define the tool with a proper MCP tool registration that includes name, description (50-200 chars), input schema with typed parameters, and output schema. Example: 'update_npc_cognitive_state' with parameters npc_id (string), player_dialogue (string), and documented return fields.
• Rename the tool to be more specific: 'update_npc_cognitive_state' or 'query_npc_behavior' instead of the generic 'execute_skill_action'. This helps LLMs understand the tool's purpose.
• Add parameter descriptions for each input. If the tool accepts npc_id and player_dialogue, document what format npc_id should be (e.g., 'NPC identifier like npc_tavern_keeper_elena'), and what player_dialogue expects (e.g., 'Player's spoken or written input to the NPC').
• Document the output schema with field types and descriptions. Return fields like affective_state (object with fear, curiosity, trust as floats 0-1), active_goal (string), dialogue_response (string), speech_audio_stream_url (URL string), turn_latency_ms (integer).
• Implement actual MCP protocol handling in mcp_server.py. The current code only prints a status message. Use a proper MCP library (e.g., mcp-python) to handle tool calls, validate inputs, and return structured responses.