This MCP server has a single tool, inspect_frame, with a basic schema and minimal description. The tool lacks proper parameter descriptions, has no output schema documentation, no error handling guidance, and the description is under 100 characters. The implementation appears to be a mock/stub (returns empty defects list) rather than a functional industrial vision system. The codebase shows no MCP-specific patterns, no tool registration framework visible, and no evidence of proper error handling or security considerations. This reads as a proof-of-concept or skeleton, not production-ready tooling.

• Expand tool description to 150 - 200 characters: explain that this tool analyzes factory/industrial images for manufacturing defects using vision ML, returns a pass/fail status and a list of detected defects with confidence scores and bounding boxes. Include when to call: 'Use this after capturing an image from an industrial camera to determine if the manufactured part meets quality standards.'
• Document the output schema explicitly. Define: defects_detected as an array of objects {defect_type: string, confidence: float, location: {x, y, width, height}}, and pass_inspection as boolean. Include example return value in docstring.
• Add constraints to 'image_frame_url': must be http:// or https://, image must be JPEG/PNG, max size 50MB, recommended dimensions 640×480 to 2048×2048.
• Define 'defect_threshold' bounds in description: 'Confidence threshold (0.0 - 1.0, default 0.85). Only defects with confidence >= threshold are reported. Lower threshold = more defects reported but higher false-positive rate.'
• Add error handling and recovery guidance: document what happens if URL is invalid (return error: 'Invalid URL format'), if image cannot be downloaded (return error: 'Failed to download image, verify URL is publicly accessible and not behind auth'), if format unsupported (return error: 'Unsupported image format, only JPEG and PNG supported').
• Replace mock implementation with actual vision inspection logic, or clearly mark this as a stub example and provide integration instructions for a real vision library.