DINO-X Image Detection MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| DINOX_API_KEY | Yes | Your DINO-X API key from the DINO-X Platform (https://cloud.deepdataspace.com/request_api) |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Server capabilities have not been inspected yet.
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| detect-objects-by-textA | Analyze an image based on a text prompt to identify and count specific objects, and return detailed descriptions of the objects and their 2D coordinates. |
| detect-all-objectsB | Analyze an image to detect all identifiable objects, returning the category, count, coordinate positions and detailed descriptions for each object. |
| detect-human-pose-keypointsC | Detects 17 keypoints for each person in an image, supporting body posture and movement analysis. |
| visualize-detection-resultB | Visualize detection results by drawing bounding boxes and labels on the original image. Images are saved to the directory specified by IMAGE_STORAGE_DIRECTORY environment variable. |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: detect-all-objects performs general object detection, detect-human-pose-keypoints focuses on human pose analysis, detect-objects-by-text enables text-guided detection, and visualize-detection-result handles visualization. There is no overlap in functionality, making tool selection straightforward for an agent.
All tool names follow a consistent verb_noun pattern with hyphens (e.g., detect-all-objects, detect-human-pose-keypoints). The naming is predictable and readable throughout, with no deviations in style or convention.
With 4 tools, the server is well-scoped for image detection tasks. Each tool earns its place by covering distinct aspects: detection (general, pose-specific, text-guided) and visualization, avoiding bloat while providing essential functionality.
The tool set covers core detection workflows (general, pose, text-guided) and visualization, with no obvious dead ends. A minor gap exists in lacking tools for modifying or deleting detection results, but agents can work around this by re-running detections or handling data externally.