Single tool 'execute_skill_action' has a verb-noun name (positive), but the implementation is severely deficient. The tool definition exists only in client.py as a mock method, not in the actual MCP server registration. The mcp_server.py contains no tool schema, no input validation, no output schema documentation, and no error handling. The description is generic and lacks WHEN/WHY context. Parameters have descriptions but no type validation in the server. The server prints a static JSON startup message and does not implement the MCP protocol, it does not handle tool calls, parse input, or return structured results. This is a non-functional skeleton.

• Implement proper MCP tool registration in mcp_server.py using the MCP SDK. Define the tool with name, description, and full JSON Schema for inputs and outputs.
• Add JSON Schema type definitions for all parameters: apparel_category as enum (list valid categories), tier3_town as enum (list valid clusters), order_bundles_count as integer with min/max bounds.
• Document the output schema with all returned fields typed (b2b_match_id: string, factory_direct_price_per_piece_inr: number, etc.) so LLMs can plan downstream actions.
• Expand tool description to 100-200 characters explaining: what the tool does (matches mills to retailers), when to use it (when a retailer needs apparel sourcing), and what it returns (pricing, credit terms, guarantees).
• Add error handling for invalid inputs (unknown apparel_category, invalid tier3_town, out-of-range order_bundles_count) with actionable error messages listing valid options.