This MCP server presents as a Flask-based REST wrapper for CATIA automation. However, it is NOT actually an MCP server, it lacks MCP protocol implementation entirely. The codebase shows Flask blueprints registering HTTP endpoints (e.g., '/api/sketch/create_sketch'), JWT authentication, and Pydantic validation, but contains zero MCP-specific code: no tool_use handling, no MCP request/response envelopes, no resource_list implementations, no prompt_list. Tools are inferred to exist based on API blueprint definitions, not explicitly registered via an MCP server interface. For the 39 tools described: most have minimal descriptions (many 20-40 chars, well below the 50-200 char baseline for LLM optimization). Parameter descriptions are present but generic ('Name of the sketch', 'Array of points'). Input schemas are readable from the rubric but NOT visible in the actual source code provided, only the API route signatures and request.get_json() extraction. No output schemas are documented. Error handling is delegated to @api_exception_handler decorators without visibility into recovery guidance. The tool definitions themselves violate MCP composition principles: 25 tools are WRITE operations with no corresponding READ/discovery tools, forcing an agent to guess parameter values; related operations like 'create_sketch' and 'draw_polyline' are separate with no cross-linking; batch operations (add_constraints_batch) exist alongside single-item variants, inviting inefficient loops. Schema scores are capped at 0 for most tools because the actual JSON Schema validation code is not shown, only Flask request parsing is visible. This is a domain-specific CAD automation service wrapped in Flask, not an MCP server.

• CRITICAL: Implement actual MCP server interface. Replace Flask blueprints with MCP tool_use message handling, CallToolRequest/CallToolResult envelopes, and proper resource/prompt lists. See https://spec.modelcontextprotocol.io/.
• CRITICAL: Expand all tool descriptions to 100-150 characters. Follow pattern: 'ACTION VERB + object + when to use + what it returns.' E.g., 'Create a new sketch on a specified plane for 2D drawing. Returns sketch ID and name. Call this before draw_polyline or draw_spline.'
• CRITICAL: Document output schemas. For each tool, specify the structure of {'result': ...}. Example: 'Returns {sketch_id: string, sketch_name: string, plane: string}'. Include this in the tool definition or a companion schema document.
• HIGH: Add READ/discovery tools: list_sketches(), list_bodies(), list_parameters(), list_constraints(), describe_part(). Agents need to discover what to operate on before calling WRITE tools.
• HIGH: Convert free-form string parameters to enums in JSON Schema. For constraint_type, declare enum=['coincident', 'contact', 'offset']. For operation in boolean_operation, enum=['union', 'subtract', 'intersect'].
• HIGH: Enhance parameter descriptions with format, range, and constraints. E.g., 'sketch_name (string, max 50 chars): Name of the target sketch. Must match an existing sketch created via create_sketch. Case-sensitive.'
• HIGH: Implement structured error responses with recovery guidance. Return {error: string, code: string, retryable: boolean, suggestion: string}. E.g., 'Sketch not found. Call list_sketches() to see available sketches.'

Inferred effective spec: <=2025-11-25.

• Relies on Logging (deprecated) - log to stderr or use OpenTelemetry