detection-mcp
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| detection-mcp | No | Arguments for the detection-mcp MCP server |
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
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {
"listChanged": true
} |
| logging | {} |
| prompts | {
"listChanged": false
} |
| resources | {
"subscribe": false,
"listChanged": false
} |
| extensions | {
"io.modelcontextprotocol/ui": {}
} |
| experimental | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| create_datasetC | Register a dataset root without changing any source image. |
| delete_datasetA | Soft-delete a dataset and preserve all related state. |
| restore_datasetB | Restore a soft-deleted dataset record. |
| list_datasetsB | List registered datasets. |
| get_datasetC | Get dataset metadata, including a deleted dataset. |
| add_categoriesB | Add categories atomically to an active dataset. |
| edit_categoryC | Change a category name or authoritative description. |
| delete_categoryB | Soft-delete a category and retain historical annotations. |
| restore_categoryC | Restore a category, optionally under a new name. |
| list_categoriesB | List categories for a dataset. |
| get_categoryB | Get a category, including a soft-deleted category. |
| list_imagesB | Discover dataset images with status filtering and stable ordering. |
| set_image_statusA | Set annotation workflow status without changing the image. |
| preview_imageC | Return an orientation-corrected preview and size metadata. |
| preview_annotationsC | Return an in-memory annotation overlay and metadata. |
| list_annotationsB | List annotations with stable filters and pagination. |
| add_bbox_annotationsC | Add normalized xyxy annotations in one transaction. |
| edit_bbox_annotationB | Edit a bbox annotation without changing its type. |
| delete_bbox_annotationA | Hard-delete one or more bbox annotations atomically. |
| add_rotated_bbox_annotationsC | Validate, correct, and add rotated annotations atomically. |
| edit_rotated_bbox_annotationC | Edit a rotated annotation without changing its type. |
| delete_rotated_bbox_annotationB | Hard-delete one or more rotated annotations atomically. |
| export_metadata_jsonlB | Preflight and atomically export completed-image metadata. |
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 23 tools
Each tool targets a distinct resource (dataset, category, image, bbox, rotated bbox, export) with clear action verbs. Even the two annotation types are unambiguously separated by bbox vs rotated_bbox in their names.
All tools follow a consistent verb_noun snake_case pattern, with clear verbs like create, delete, list, edit, add, get, restore, set, preview, export. Minor pluralization inconsistencies (e.g., add_bbox_annotations vs edit_bbox_annotation) do not undermine overall predictability.
At 23 tools, the server sits in the heavy range (16-25). However, the number is justified by the broad domain covering datasets, categories, images, two annotation types, and export, so it feels borderline rather than excessive.
The surface covers full CRUD lifecycles for datasets, categories, and both annotation types, plus image status management and export. A notable gap is the lack of a dataset update operation, but core workflows are well-supported.