# Roboflow MCP Server

> A hosted Model Context Protocol (MCP) server that exposes Roboflow's computer vision platform — projects, datasets, images, annotations, versions, models, and Workflows — as tools for AI assistants.

This server speaks MCP over streamable HTTP at `https://mcp.roboflow.com/mcp`. Authenticate with a Roboflow API key sent in the `x-api-key` header. Get a key at https://app.roboflow.com/settings/api.

## Connection
- [MCP endpoint](https://mcp.roboflow.com/mcp): streamable-http transport, requires the `x-api-key` header
- [Homepage](https://mcp.roboflow.com/): install snippets for Claude Code, Claude Desktop, and other MCP clients
- [Get a Roboflow API key](https://app.roboflow.com/settings/api)

## Tool categories
- **Projects** — manage projects in your workspace
  - `projects_list`: List all projects in the workspace associated with the API key.
  - `projects_create`: Create a new computer vision project.
  - `projects_get`: Get detailed info about a project including versions, classes, splits, and trained models.
- **Images** — prepare image uploads for a project
  - `images_prepare_upload`: Get an upload URL to upload a single image to a project.
  - `images_prepare_upload_zip`: Prepare a zip upload of images and annotations to a project.
  - `images_upload_zip_status`: Check the status of a zip upload task.
  - `images_search`: Search for images inside a project.
- **Annotations** — save annotations to a project image
  - `annotations_save`: Save an annotation for an existing image.
- **Batch** — organize images into batches and create labeling jobs
  - `annotation_batches_list`: List upload batches in a project.
  - `annotation_batches_get`: Get details about a specific batch.
  - `annotation_jobs_create`: Create an annotation job to assign a batch of images to a labeler.
- **Versions** — create and inspect dataset versions
  - `versions_generate`: Create a new dataset version with optional preprocessing and augmentation.
  - `versions_get`: Get info about a dataset version including splits and model metrics.
  - `versions_export`: Check or trigger a dataset export for a version.
- **Models** — train models and monitor training progress
  - `models_list`: List trained models associated with a project.
  - `models_get`: Get details for a trained model.
  - `models_infer`: Run hosted inference on an image using a trained model.
  - `models_train`: Start training a model on a dataset version.
  - `models_get_training_status`: Get the training progress and metrics for a dataset version.
- **Workflows** — build and execute inference pipelines
  - `workflows_list`: List saved workflows in the current workspace.
  - `workflows_get`: Get details for a saved workflow.
  - `workflows_create`: Create and save a new Workflow in the workspace.
  - `workflows_update`: Update an existing saved Workflow's name and definition.
  - `workflow_blocks_list`: List all available Workflow blocks with a short summary of each.
  - `workflow_blocks_get_schema`: Get the full schema of a specific Workflow block.
  - `workflow_specs_validate`: Validate a Workflow JSON definition without executing it.
  - `workflows_run`: Execute a saved Workflow on one or more images.
  - `workflow_specs_run`: Execute a Workflow from an inline JSON definition.
- **Universe** — search public datasets on Roboflow Universe
  - `universe_search`: Search Roboflow Universe for datasets or models.
- **Meta** — report issues or suggestions
  - `meta_feedback_send`: Report a bug, missing feature, UX friction, or documentation issue.

## Optional
- [Roboflow platform documentation](https://docs.roboflow.com)
- [Model Context Protocol specification](https://modelcontextprotocol.io)
- [Roboflow homepage](https://roboflow.com)