roboflow

Endpoint

Hosted endpoint · transport http

Connect

This is a remote MCP server — point an MCP client at the endpoint URL. Clients with native remote support (Claude, Cursor, VS Code, …):

For stdio-only clients (older Claude Desktop, etc.), bridge with mcp-remote:

If the server requires authentication, add the provider's token/header (e.g. an X-API-Key) per its docs. A quick reachability check:

Tools

• agent_chat — Chat with the Roboflow AI agent (Roboflow Q&A, advanced Workflow building, CV solution planning).
• agent_conversation_get — Get a single agent conversation with its full message history.
• agent_conversations_list — List Roboflow agent conversations in the workspace.
• agent_workflow_publish — Publish the latest agent-edited draft of a workflow.
• projects_list — List all projects in the workspace associated with the API key.
• projects_get — Get detailed info about a project including versions, classes, splits, and trained models.
• projects_create — Create a new computer vision project (detection, classification, segmentation, or keypoint).
• projects_fork — Enqueue an async fork of a public Universe project into your workspace.
• projects_health — Get the dataset health check for a project.
• create_project_app — Open a Prefab UI so the human can create a Roboflow project (calls projects_create on confirm).
• images_search — Search for images inside a project with filters for tag, class, batch, annotation job, and visual similarity.
• images_prepare_upload — Get a pre-built upload URL to upload a single image to a project via curl.
• images_prepare_upload_zip — Prepare a signed URL to bulk-upload a zip of images (and optional COCO/YOLO/VOC/folder annotations).
• images_upload_zip_status — Check the status of a zip upload task started with images_prepare_upload_zip.
• annotations_save — Save an annotation (XML, JSON, or text) for an existing image.
• 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.
• annotation_jobs_create — Create an annotation job to assign a batch of images to a labeler and reviewer.
• annotation_batches_list — List upload batches in a project.
• annotation_batches_get — Get details about a specific batch including image count and status.
• versions_get — Get info about a dataset version including splits, preprocessing, augmentation, and model metrics.
• versions_generate — Create a new dataset version with optional preprocessing and augmentation settings.
• versions_export — Check or trigger a dataset export for a version in a specific format (e.g. coco, yolov8, jsonl).
• models_list — List trained models associated with a project.
• models_get — Get details for a trained model.
• models_train — Start training a model (RF-DETR, YOLO, etc.) on a dataset version.
• models_infer — Run hosted inference on an image using a trained model and return JSON predictions.
• models_get_training_status — Get the training progress and metrics (epoch, mAP, loss) for a dataset version.
• models_star_nas — Star or unstar a NAS-trained model.
• trainings_get_results — Get the run-level training results bundle.
• trainings_stop — Request an early stop on an in-flight training run.
• trainings_cancel — Cancel an in-flight training run.
• 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_confusion_matrix — Get the confusion matrix 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_image_predictions — Get per-image prediction stats for an eval (paginated).
• model_evals_get_vector_analysis — Get UMAP + HDBSCAN clustering of image embeddings for an eval.
• model_evals_get_recommendations — Get the LLM-generated recommendations for an eval, if available.
• 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 from a JSON definition.
• workflows_update — Update an existing saved Workflow's name and definition.
• workflows_run — Execute a saved Workflow by ID on one or more images.
• workflow_blocks_list — List all available Workflow blocks with a short summary of each.
• workflow_blocks_get_schema — Get the full schema (properties, required fields) of a specific Workflow block.
• workflow_specs_validate — Validate a Workflow JSON definition without executing it.
• workflow_specs_run — Execute a Workflow directly from an inline JSON definition without saving it first.
• devices_list — List devices registered in the workspace.
• devices_get — Get a single device by id.
• devices_create — Provision a new v2 device.
• devices_get_config — Get the device's current runtime configuration.
• devices_get_default_config — Get the workspace's default device configuration.
• devices_get_config_history — List prior configuration revisions, newest first.
• devices_update_config — Update the device's runtime configuration.
• devices_get_logs — Fetch device logs from Elasticsearch.
• devices_get_events — List device and stream lifecycle events.
• devices_get_telemetry — Get aggregated hardware metrics (cpu, memory, disk, gpu).
• devices_streams_list — List streams configured on the device.
• devices_streams_get — Get a single stream by id.
• universe_search — Search Roboflow Universe for public datasets or models, with operators for class, image count, project type, and sort.
• universe_search_app — Open a Prefab Universe UI for search, visual comparison, image skim, and fork - the human picks the dataset.
• universe_dataset_images_search — Search images inside a public Universe dataset URL.
• async_tasks_get — Poll an async task by id (e.g. project forks). Poll every 5 seconds; processing may take up to 30 seconds to start.
• meta_feedback_send — Report a bug, missing feature, UX friction, or documentation issue back to the Roboflow team.

About MCP

The Model Context Protocol (MCP) is an open protocol Anthropic introduced for connecting LLM-based agents to external tools and data sources. Providers publish MCP servers that expose their API surface as structured, discoverable tools — an MCP-compatible client (Claude Desktop, Cursor, Cline, Continue, etc.) can connect to the server and call its tools without any per-provider integration code.

Browse every MCP server on the APIs.io network or compare with the broader Agent Skill surfaces of the same providers.

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