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N8N Compatible SSE Server for YOLO object detection with vision capabilities
This MCP server has significant definition quality gaps. Both tools have basic schemas and descriptions, but lack depth, parameter clarity, and actionable error handling. The naming is reasonably clear (verb-prefixed), but descriptions are minimal and parameter documentation is sparse. No output schema is documented, and error handling provides generic messages without recovery guidance. The code shows basic tool registration via the MCP SDK, but the definitions fall well short of production quality.
Detect objects in an image or video using YOLO
Execute Ultralytics YOLO CLI commands
Output schemas not documented. LLMs cannot plan downstream steps or extract expected fields. Both tools return generic text responses without structured schema documentation.
Parameter descriptions are minimal and lack format/constraint guidance. 'image_path' has no length constraints, format expectations, or allowed file types. 'args' for yolo_cli has no validation hints, character limits, or examples of valid command structures.
Tool descriptions are under 100 characters and lack actionable context. 'Detect objects in an image or video using YOLO' does not explain when to use this tool vs alternatives, what formats are supported, or what the LLM should expect in the response.
Inferred effective spec: <=2025-11-25.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-22 | F | 48 | <=2025-11-25 | v2 |
| 2026-03-09 | F | 0 | - | v1 |
yolo_cli tool accepts arbitrary shell arguments with no validation. This is a command injection risk. The tool description does not warn about escaping, allowed command syntax, or safe vs unsafe argument patterns. LLMs could be tricked into passing malicious payloads.
Error responses are generic and non-actionable. Both tools return plain error messages like 'File not found' or 'YOLO service error' without recovery guidance. LLMs cannot determine whether to retry, ask the user, or give up.
detect_objects 'manual_result' parameter is poorly explained. It is unclear when or why an LLM would use a manual override, whether it bypasses validation, or what format it expects. This invites misuse.
No output pagination or result limits documented. detect_objects does not state what happens if an image contains thousands of objects. yolo_cli does not bound command execution time, memory, or output size. Large responses risk context window exhaustion.