The server exposes a single tool 'execute_skill_action' with no visible input schema, no parameter descriptions, and a generic description (58 chars). The mcp_server.py file contains only a stub that prints a JSON status message, it does not implement the MCP protocol, handle tool calls, or define schemas. The tool definition is inferred from the supported_tools list, not explicitly registered with schema. No error handling, no output schema documentation, and no parameter validation visible. This is a non-functional skeleton.

• Add explicit JSON Schema input definition with type, description, and constraints for each parameter (natural_language_query: string, schema_context: object optional). Include enums or patterns where applicable.
• Expand tool description to 100-200 chars explaining: (1) what the tool does (routes queries across tabular, graph, and semantic search), (2) when to use it (when you need to find entities matching multiple criteria), (3) what it returns (query plan + result rows with similarity scores).
• Document each parameter: 'natural_language_query (string, required): The user's question in natural language, e.g. "Find FinTech companies with ARR over $1M using LangChain". schema_context (object, optional): Database schema hints to guide query routing.'
• Add output schema documentation: 'Returns {query_plan: {tabular_filter, graph_traversal, semantic_search}, result_rows: [{company, arr_usd, uses_langchain, similarity_score}], query_type_used: string}'.
• Rename tool from 'execute_skill_action' to 'route_postgres_query' or 'search_hybrid_graph' to clarify intent and match verb_noun naming convention.