Single tool 'execute_skill_action' has critical definition gaps. Input schema is present but severely underdeveloped: only 2 parameters with minimal type info (both generic object/string). No output schema documented anywhere. Description is 169 chars but lacks WHEN/WHY guidance and does not explain what 'ambiguity repair' means to an LLM. No parameter descriptions visible in schema. Tool definition appears inferred from client.py mock rather than explicit MCP registration. No error handling, validation rules, or recovery guidance. This server reads as a proof-of-concept stub, not production-ready tooling.

• Add explicit output schema to tool definition documenting all response fields (normalization_id, schema_validation_passed, normalized_json_object, ambiguity_repair_performed, strict_typed_schema_spec_url) with types and descriptions.
• Add descriptions to both input parameters. For 'target_json_schema_definition', clarify format: 'A JSON Schema object (draft-07 or later) defining target field names, types, and constraints. E.g. {"origin": {"type": "string"}, "passenger_count": {"type": "integer"}}'.
• Rename tool to 'normalize_to_schema' or 'transform_unstructured_to_json' to clearly signal the normalization action and distinguish from generic 'execute' verbs.
• Implement actual tool invocation in mcp_server.py. Currently it only prints a static status. Add request/response handling, parameter validation, and error responses with actionable guidance (e.g., 'Schema validation failed: field "origin" is required but missing from normalized output').
• Document error cases and recovery paths in tool description. E.g., 'If ambiguity_repair_performed is false, the input was too ambiguous to normalize reliably, ask the user for clarification on the ambiguous fields.'