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nickedridge-wq

Roboflow MCP Server

by nickedridge-wq
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Python
Remote

roboflow-mcp

A Model Context Protocol (MCP) server that exposes the Roboflow platform API as tools in Claude Code. Manage datasets, trigger training runs, search Universe, and run inference — all from the CLI.

Setup

Requirements: Python 3.10+, a Roboflow API key, Claude Code installed.

git clone https://github.com/nickedridge-wq/roboflow-mcp.git
cd roboflow-mcp
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

Configure Claude Code

Option A — project-level (recommended, checked into the repo):

claude mcp add roboflow \
  --env ROBOFLOW_API_KEY=your_api_key_here \
  -- /path/to/roboflow-mcp/venv/bin/python /path/to/roboflow-mcp/server.py

This writes a .mcp.json file in the current project directory.

Option B — user-level (available in all projects):

claude mcp add roboflow --scope user \
  --env ROBOFLOW_API_KEY=your_api_key_here \
  -- /path/to/roboflow-mcp/venv/bin/python /path/to/roboflow-mcp/server.py

Restart Claude Code — the mcp__roboflow__* tools will be available immediately.

Tools

Tool

Description

list_workspaces

Show workspace name, URL slug, and project count

list_projects

List all projects in a workspace with type and image counts

get_project

Get classes, annotation type, and metadata for a project

list_versions

List all dataset versions with image counts per split

upload_image

Upload an image and optional annotation to a project

create_version

Generate a new dataset version with preprocessing and augmentation

download_dataset

Download a version locally (yolov8, coco, voc, and more)

download_universe_dataset

Download a public dataset directly from Roboflow Universe

search_universe

Search Universe for public datasets and pre-trained models

run_inference

Run inference via a deployed model on a local file or URL

get_model_metrics

Fetch mAP, precision, and recall for a trained version

Example Workflows

Find and download a public dataset

search_universe("hard hat detection")
→ pick a result, note workspace + project + version

download_universe_dataset(
  universe_workspace="roboflow-universe-projects",
  universe_project="hard-hat-universe",
  version_number=1,
  model_format="yolov8",
  location="./datasets/hard-hat"
)

Upload images and generate a training version

upload_image(project_url="my-project", image_path="/data/img001.jpg",
             annotation_path="/data/img001.xml", split="train")

create_version(
  project_url="my-project",
  preprocessing={"auto-orient": True, "resize": {"width": 640, "height": 640, "format": "Stretch to"}},
  augmentation={"flip": {"horizontal": True}, "rotation": {"degrees": 15}}
)

Run inference and check model performance

run_inference(project_url="my-project", version_number=3,
              image_path="/data/test.jpg", confidence=60)

get_model_metrics(project_url="my-project", version_number=3)

Tests

python -m unittest test_server -v

21 tests covering output suppression, lazy init thread safety, input validation, null model guard, auth error propagation, and parameter contracts. No live API key required.

Implementation Notes

  • Lazy authentication — the Roboflow SDK authenticates once per session on first tool call, with double-checked locking for thread safety.

  • Output suppression — the SDK prints to both stdout and stderr on init, which corrupts MCP's stdio transport. All SDK calls redirect both streams.

  • search_universe and get_model_metrics call the Roboflow REST API directly for endpoints not exposed cleanly through the SDK.

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