detection-mcp
Click on "Install Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@detection-mcplist all images in the training dataset that are still pending annotation"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
detection-mcp
detection-mcp is a local STDIO Model Context Protocol server for object-detection annotation. It stores workflow state and annotations in SQLite, renders review previews in memory, and exports JSONL without modifying source images.
Status
The v1 implementation is available for review. It exposes 23 tools for datasets, categories, images, axis-aligned boxes, rotated boxes, previews, and export. The package targets Python 3.12 or newer and uses fastmcp>=3.4.7,<4.0.0, allowing compatible 3.x security updates.
Related MCP server: z_ai_vision_mcp_server_clone
Install from this repository
Install uv, then run:
uv tool install .
detection-mcp --versionFor repository development:
uv sync --locked --all-groups
uv run pre-commit install --install-hooks --hook-type pre-commit --hook-type commit-msg --hook-type pre-pushConfigure an MCP client
Use an installed executable and grant only the directories the task needs:
{
"mcpServers": {
"detection-mcp": {
"command": "detection-mcp",
"args": [
"--db-path", "/var/lib/detection-mcp/annotations.db",
"--allowed-dataset-root", "/srv/datasets",
"--allowed-export-root", "/srv/exports"
]
}
}
}The server uses STDIO: protocol messages go to stdout and logs go to stderr. CLI options override DETECTION_MCP_* environment variables. See configuration for all settings.
Annotation workflow
Register a dataset root and define categories.
List images by status or deterministic random order.
Preview an orientation-corrected image.
Add normalized axis-aligned or rotated boxes in atomic batches.
Preview the overlay and correct annotations before marking the image completed.
Export completed images to AutoTrain or extended JSONL.
Install the Agent Skills
The Agent Skills live only in the repository's root skills/ directory. They are not included in the Python wheel, source distribution, or container image. Install either Skill directly from GitHub with the skills CLI:
npx skills add ryan-minato/detection-mcp --skill object-detection-annotation
npx skills add ryan-minato/detection-mcp --skill detection-mcp-setupThe default installation is project-local. Add --global when the Skill should be available across projects.
Tool groups
Area | Tools |
Datasets |
|
Categories |
|
Images and review |
|
Annotations |
|
Export |
|
See the complete tool reference and export format.
Development commands
just is the repository command executor:
just sync # synchronize the locked environment
just test # run ordinary tests
just quality-control # run CI checks without tests
just quality # run the complete local commit gate
just hooks # run repository hooks against tracked files
just check # run quality and hooksNever bypass Git hooks. Every commit requires the full quality gate and a staged secret/PII scan. See CONTRIBUTING.md, SECURITY.md, and AGENTS.md.
Containers
The production image runs as a non-root user. Dataset mounts must be read-only, while state and exports require separate writable mounts. See Docker deployment and docker-compose.example.yml.
License
Apache-2.0. See LICENSE.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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