# 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`. Clients can connect via an MCP URL only and will be prompted to sign in to Roboflow.

## Connection
- [MCP endpoint](https://mcp.roboflow.com/mcp): streamable-http transport, supports MCP OAuth discovery
- [OAuth protected-resource metadata](https://mcp.roboflow.com/.well-known/oauth-protected-resource/mcp)
- [Homepage](https://mcp.roboflow.com/): install snippets for Claude Code, Claude Desktop, and other MCP clients

## Tool categories
- **Agent** — chat with the Roboflow AI agent for Roboflow Q&A, advanced Workflow building, and CV solution planning
  - `agent_chat`: Chat with the Roboflow AI agent.
  - `agent_conversations_list`: List Roboflow agent conversations in the workspace.
  - `agent_conversation_get`: Get a single agent conversation with its message history.
  - `agent_workflow_publish`: Publish the latest agent-edited draft of a workflow.
- **Projects** — manage projects in your workspace
  - `projects_list`: List projects in the workspace associated with the credential.
  - `projects_create`: Create a new computer vision project.
  - `projects_health`: Get dataset health check for a project.
  - `projects_get`: Get detailed info about a project including versions, classes, splits, and trained models.
  - `projects_fork`: Enqueue an async fork of a public Universe project into your workspace.
- **Project Deployment** — launch and manage a stable live endpoint for a project
  - `project_deployment_get`: Get the stable live endpoint state for a project.
  - `project_deployment_launch`: Create or prepare a stable Project Deployment.
  - `project_deployment_set_model`: Change the default model behind a Project Deployment without changing its endpoint.
  - `project_deployment_run`: Run inference through the project's stable live endpoint.
  - `project_deployment_enable_active_learning`: Enable Active Learning for a Project Deployment.
  - `project_deployment_disable_active_learning`: Pause Active Learning collection for a Project Deployment.
  - `project_deployment_configure_active_learning`: Configure how a Project Deployment collects Active Learning data.
  - `project_deployment_list_review_queues`: List Active Learning review queues fed by production inference.
- **Images** — upload, search, and manage images across projects
  - `image_upload`: Upload local image files to a project via a zip.
  - `image_upload_status`: Check the status of an image zip upload task.
  - `images_update_metadata`: Update metadata and tags on a single image. Metadata and tags can be useful to find images later in the platform.
  - `images_batch_update_metadata`: Batch-update metadata and tags on multiple images (async).
  - `images_workspace_search`: Search images across the entire workspace using RoboQL.
  - `images_search`: Search for images inside a project.
  - `universe_dataset_images_search`: Search images inside a public Universe dataset given its URL.
- **Media Understanding** — analyze images and short videos with a vision model to scope CV tasks, suggest classes, and plan workflows
  - `media_analyze`: Analyze an image or short video with a vision model to understand its contents.
  - `media_upload`: Prepare a local image or video for analysis with media_analyze.
  - `media_trim`: Cut a clip from an Asset Library video and get an analyzable public url.
  - `media_upload_finalize`: Validate a staged upload (from media_upload) and publish it.
- **Annotations** — save annotations to a project image
  - `annotations_save`: Save an annotation for an existing image.
- **Auto-label** — kick off and track hosted auto-labeling jobs
  - `autolabel_start`: Start a hosted auto-label job over a batch of images.
  - `autolabel_job_get`: Get per-subjob status and progress for an auto-label job.
- **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 its trainings.
  - `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.
  - `trainings_create`: Create a training on a dataset version.
  - `models_upload_custom_weights`: Upload custom model weights to Roboflow.
  - `trainings_list`: List the trainings on a dataset version.
  - `trainings_get`: Get a training's status, produced models, and metrics.
  - `trainings_cancel`: Cancel an in-flight training run.
  - `trainings_stop`: Request an early stop on an in-flight training run.
  - `models_star_nas`: Star or unstar a NAS-trained model.
- **Model Evaluations** — inspect held-out evaluation results for trained models — mAP, precision/recall sweeps, per-class breakdowns, confusion matrices, vector clustering, and per-image predictions
  - `model_evals_list`: List model evaluations in the current workspace.
  - `model_evals_get`: Get the top-level summary for a single model evaluation.
  - `model_evals_get_map_results`: Get per-split mAP results for an eval.
  - `model_evals_get_confidence_sweep`: Get the precision/recall/F1 confidence sweep for an eval.
  - `model_evals_get_performance_by_class`: Get per-class performance metrics for a single split.
  - `model_evals_get_confusion_matrix`: Get the confusion matrix for an eval.
  - `model_evals_get_vector_analysis`: Get UMAP + HDBSCAN clustering of image embeddings for an eval.
  - `model_evals_get_image_predictions`: Get per-image prediction stats for an eval (paginated).
  - `model_evals_get_recommendations`: Get the LLM-generated recommendations for an eval, if available.
- **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.
- **Devices** — list, inspect, and provision Roboflow Deployment Manager devices via the external Deployments v2 API
  - `devices_list`: List devices registered in the workspace.
  - `devices_create`: Provision a new v2 device.
  - `devices_get`: Get a single device by id.
  - `devices_get_config`: Get the device's current runtime configuration.
  - `devices_get_default_config`: Get the workspace's default device configuration.
  - `devices_update_config`: Update the device's runtime configuration.
  - `devices_get_config_history`: List prior configuration revisions, newest first.
  - `devices_streams_list`: List streams configured on the device.
  - `devices_streams_get`: Get a single stream by id.
  - `devices_get_logs`: Fetch device logs from Elasticsearch.
  - `devices_get_telemetry`: Get aggregated hardware metrics (cpu, memory, disk, gpu).
  - `devices_get_events`: List device and stream lifecycle events.
- **API Keys** — manage workspace API keys
  - `api_keys_list`: List all API keys for the workspace.
  - `api_keys_get`: Get metadata for a single API key by its keyId.
  - `api_keys_get_publishable`: Get the workspace's publishable key (``rf_<workspaceId>``).
  - `api_keys_create`: Create a new API key for the workspace.
  - `api_keys_update`: Update the name, scopes, or custom_metadata of an existing API key.
  - `api_keys_protect`: Mark an API key as protected so it cannot be revoked or disabled.
  - `api_keys_disable`: Disable (or re-enable) an API key without revoking it.
  - `api_keys_revoke`: Permanently revoke (delete) an API key.
- **Vision Events** — query and report on production vision events (quality checks, inventory counts, safety alerts, operator feedback) and manage the use cases they are grouped under
  - `vision_events_query`: Query production vision events for a use case, with filters and pagination.
  - `vision_events_use_cases_list`: List the vision-event use cases in the workspace.
  - `vision_events_custom_metadata_schema_get`: Get the custom-metadata schema discovered for a use case.
  - `vision_events_use_case_create`: Create a new vision-event use case.
  - `vision_events_use_case_rename`: Rename an existing vision-event use case.
  - `vision_events_use_case_archive`: Archive a vision-event use case.
  - `vision_events_use_case_unarchive`: Restore a previously archived vision-event use case.
- **Universe** — search public datasets on Roboflow Universe
  - `universe_search`: Search Roboflow Universe for datasets or models.
- **Datasources** — configure and run cloud-bucket mirroring into projects
  - `datasources_list`: List datasource (cloud-bucket mirror) configurations in the workspace.
  - `datasource_get`: Get full detail for a single datasource, including its bucket,
  - `datasource_validate`: Check that Roboflow can access the datasource's bucket.
  - `datasource_job_get`: Get the status and statistics of a single datasource mirror run.
  - `datasource_create`: Create a datasource that mirrors a cloud bucket into your workspace.
  - `datasource_update`: Update a datasource. Only the fields you pass are changed.
  - `datasource_delete`: Delete a datasource configuration. Does not delete already-mirrored images.
  - `datasource_trigger`: Start a mirror run for a datasource.
  - `connect_cloud_storage`: Connect a cloud bucket end to end in one call: reuse or create a credential, create the datasource, validate it, and start the first mirror run.
- **Credentials** — manage cloud-storage credentials used by datasources
  - `credentials_list`: List cloud-storage credentials in the workspace.
  - `credentials_delete`: Delete a credential. Datasources still referencing it will fail to run.
  - `credentials_create`: Create a cloud-storage credential.
- **Async tasks** — poll the status of long-running async operations
  - `async_tasks_get`: Poll an async task by id. Poll every 5 seconds; processing may take up to 30 seconds to start.
- **Meta** — report issues or suggestions
  - `meta_feedback_send`: Send product feedback about Roboflow to the Roboflow team.

## Skills (expert knowledge as MCP resources)
- **api-reference** — Protocol-level facts for Roboflow REST and Inference APIs — URL patterns, auth, parameters, error codes, and SDK quick-start. For deployment strategy and Workflow execution patterns, see roboflow-inference.
  - [api-reference/SKILL](roboflow://skills/api-reference/SKILL)
  - [api-reference/api-key-management](roboflow://skills/api-reference/api-key-management)
  - [api-reference/inference](roboflow://skills/api-reference/inference)
  - [api-reference/rest-api](roboflow://skills/api-reference/rest-api)
- **cloud-storage** — Use when connecting cloud storage (AWS S3 / S3-compatible or Google Cloud Storage) to Roboflow to mirror images into a workspace — creating storage credentials, defining datasources (bucket-mirror configs), selecting objects with glob rules, validating access, and running/scheduling mirror jobs.
  - [cloud-storage/SKILL](roboflow://skills/cloud-storage/SKILL)
- **data-management** — Use when uploading images, labeling, organizing datasets, creating Roboflow projects (detection/segmentation/keypoint/classification), tags, splits, versions, or RoboQL search.
  - [data-management/SKILL](roboflow://skills/data-management/SKILL)
  - [data-management/labeling](roboflow://skills/data-management/labeling)
- **inference** — Deployment option comparison (serverless, dedicated, self-hosted, batch) and Workflow execution patterns. For raw API URL patterns, auth, and request/response formats, see roboflow-api-reference.
  - [inference/SKILL](roboflow://skills/inference/SKILL)
  - [inference/batch-jobs](roboflow://skills/inference/batch-jobs)
  - [inference/batch-staging](roboflow://skills/inference/batch-staging)
  - [inference/local-tooling](roboflow://skills/inference/local-tooling)
  - [inference/workflow-templates](roboflow://skills/inference/workflow-templates)
  - [inference/workflows](roboflow://skills/inference/workflows)
- **plans-and-pricing** — Use when answering questions about Roboflow plans, credit usage, or cost estimation; directs users to roboflow.com/pricing for current dollar amounts.
  - [plans-and-pricing/SKILL](roboflow://skills/plans-and-pricing/SKILL)
- **product-navigation** — Use when explaining where Roboflow features live in the app.roboflow.com web app, mapping intents like upload, annotate, train, deploy to specific page URLs.
  - [product-navigation/SKILL](roboflow://skills/product-navigation/SKILL)
  - [product-navigation/features-by-page](roboflow://skills/product-navigation/features-by-page)
- **training-and-evaluation** — Use when training Roboflow models, improving accuracy, or setting up a production feedback loop — covers architecture selection, model IDs, checkpoints, evaluation metrics, the iterative improvement playbook, and active learning via the Dataset Upload workflow block.
  - [training-and-evaluation/SKILL](roboflow://skills/training-and-evaluation/SKILL)
  - [training-and-evaluation/active-learning](roboflow://skills/training-and-evaluation/active-learning)
  - [training-and-evaluation/improvement-playbook](roboflow://skills/training-and-evaluation/improvement-playbook)
- **universe** — Use when searching for or using public datasets/models on Roboflow Universe (universe.roboflow.com), the open repository of 1M+ computer vision datasets and 50K+ pre-trained models.
  - [universe/SKILL](roboflow://skills/universe/SKILL)

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