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MCP live v1.0.1 streamable-http

Roboflow (Official)

com.roboflow/roboflow-mcp

Roboflow computer vision for AI assistants: datasets, annotation, versioning, workflows, inference.

Uptime
20.0%
15 probes
Response
789ms
last probe
Tools
67
callable

Tools · 67

agent_chat

Chat with the Roboflow AI agent. Use this tool for: - **Roboflow Q&A** — the agent has the full Roboflow documentation indexed (SDKs, REST API, deployment options, training, batch processing, Un…

agent_conversations_list

List Roboflow agent conversations in the workspace. Returns conversation metadata (id, name, source, timestamps, linked workflow ids). Use ``agent_conversation_get`` to load the full message history …

agent_conversation_get

Get a single agent conversation with its full message history. Returns the conversation metadata and the ordered list of messages (role + text parts). Pass the same ``conversation_id`` back to ``agen…

agent_workflow_publish

Publish the latest agent-edited draft of a workflow. The agent never publishes on its own — every workflow it creates or edits is saved as a draft. This tool promotes the latest draft to a published …

projects_list

List all projects in the workspace associated with the API key. Returns a list of projects with id, name, type, image count, and more.

projects_create

Create a new computer vision project. Returns the created project's id, name, type, and url.

projects_health

Get dataset health check for a project. Running this for the first time or with regenerate=True might take up to a couple minutes. Returns class balance, image quality issues, annotation statistics,…

projects_get

Get detailed info about a project including versions, classes, splits, and trained models. Returns full project details with workspace, project info, versions, and classes.

projects_fork

Enqueue an async fork of a public Universe project into your workspace. Provide either a Universe `url` or `source_project`. Returns the platform's 202 payload verbatim — at minimum `{ taskId, url }`…

images_prepare_upload

Get an upload URL to upload a single image to a project. Returns a pre-built upload URL and instructions. The caller must perform the actual upload using curl since the MCP server cannot access local…

images_prepare_upload_zip

Prepare a zip upload of images and annotations to a project. Supports zip archives containing images with COCO, YOLO, Pascal VOC, or classification-by-folder annotations. Up to 2 GB / 10k files. Ret…

images_upload_zip_status

Check the status of a zip upload task. Returns status (created, running, completed, failed), progress, and result when completed (uploaded count, duplicates, annotation errors, etc.).

images_search

Search for images inside a project.

universe_dataset_images_search

Search images inside a public Universe dataset URL. The MCP app runs inside a host iframe, so URL parsing belongs on the server. This tool accepts the selected Universe result URL and derives the wor…

annotations_save

Save an annotation for an existing image.

autolabel_start

Start a hosted auto-label job over a batch of images. Returns ``{jobId, annotationJobId, message}``. Poll progress with ``autolabel_job_get`` using the returned ``jobId``.

autolabel_job_get

Get per-subjob status and progress for an auto-label job. Returns status, model/project type, image counts, subjob progress, ontology, confidence thresholds, and the linked annotation job id.

annotation_batches_list

List upload batches in a project. Returns a list of batches with id, name, image count, and upload info.

annotation_batches_get

Get details about a specific batch. Returns batch details including image count and status.

annotation_jobs_create

Create an annotation job to assign a batch of images to a labeler. Returns the created job details including id, name, and status.

versions_generate

Create a new dataset version with optional preprocessing and augmentation. IMPORTANT: Before calling this tool, you MUST ask the user which preprocessing and augmentation options they want to apply. …

versions_get

Get info about a dataset version including splits and model metrics. Returns version details with id, name, images, splits, preprocessing, augmentation, and model info if trained.

versions_export

Check or trigger a dataset export for a version.

models_list

List trained models associated with a project. Each row carries metrics and (for NAS children) `nasFamily`, `group`, `train.results.{hardware,latency,map5095,paretoOptimalFor}`, plus a derived `recom…

models_get

Get details for a trained model.

models_infer

Run hosted inference on an image using a trained model. Returns JSON predictions only. For visualized/annotated images, use workflow_specs_run with a visualization block instead.

models_train

Start training a model on a dataset version. IMPORTANT: A dataset version must exist before training. Use the versions_generate tool first to create one with the desired preprocessing and augmentatio…

models_get_training_status

Get the training progress and metrics for a dataset version. Use this tool to check on a training job started with models_train. Returns training status, progress (current/total epochs), latest metr…

trainings_get_results

Run-level training results bundle. For NAS sweeps (one Training produces many child Models) returns: ``{ trainingId, status, modelGroup, modelCount, recommendedByHardware: {[hardware]: modelId}, …

trainings_cancel

Cancel an in-flight training run. Works for any architecture. For NAS runs the underlying handler accepts the ``mining`` status — same call cancels mining or training phases.

trainings_stop

Request an early stop on an in-flight training run. Distinct from cancel: the run finishes the current phase gracefully (mining or training) instead of terminating immediately.

models_star_nas

Star or unstar a NAS-trained model. NAS-only by design — the server rejects non-NAS modelTypes with a ``MODEL_NOT_NAS`` error. Starring triggers TRT compilation for the model's recommended hardware s…

model_evals_list

List model evaluations in the current workspace. Returns ``{evals: [...]}`` where each entry has evalId, status, project (URL slug), versionId, modelId, and createdAt. Use ``model_evals_get`` for the…

model_evals_get

Get the top-level summary for a single model evaluation. Returns the eval metadata plus a ``summary`` of mAP / precision / recall on done evals, and an ``app_url`` deep link into the UI panel.

model_evals_get_map_results

Get per-split mAP results for an eval. Returns ``{splits: {train, valid, test}}`` where each split has overall ``map50`` / ``map50_95`` / ``map75``, ``byObjectSize`` (small/medium/large), and ``perCl…

model_evals_get_confidence_sweep

Get the precision/recall/F1 confidence sweep for an eval. For each split returns ``perThreshold`` (metrics keyed by threshold like ``"0.20"``), the ``optimalThreshold`` and ``optimalMetrics``, and ``…

model_evals_get_performance_by_class

Get per-class performance metrics for a single split. Returns ``{split, classes: [{className, map50, map50_95, map75, precision, recall, f1, optimalThreshold}, ...]}``.

model_evals_get_confusion_matrix

Get the confusion matrix for an eval. Returns ``{split, confidenceThreshold, classes, matrix}`` where ``matrix[actual][predicted]`` is the count.

model_evals_get_vector_analysis

Get UMAP + HDBSCAN clustering of image embeddings for an eval. Returns ``{clustering, preprocessing, clusters: [...]}`` — useful for finding pockets of poor-performing images. Each cluster carries ``…

model_evals_get_image_predictions

Get per-image prediction stats for an eval (paginated). Returns ``{split, confidenceThreshold, totalImages, offset, limit, images: [...]}``. Each image carries TP/FP/FN counts, precision, recall, F1,…

model_evals_get_recommendations

Get the LLM-generated recommendations for an eval, if available. Returns ``{generated: false}`` until the recommendations job has run. When ready, returns ``{generated: true, generatedAt, recommendat…

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. IMPORTANT: Always validate the config with workflow_specs_validate before creating the workflow. The config is the same JSON format used by workflow_…

workflows_update

Update an existing saved Workflow's name and definition. IMPORTANT: Always validate the config with workflow_specs_validate before updating the workflow. Use workflows_get to retrieve the current wo…

workflow_blocks_list

List all available Workflow blocks with a short summary of each. Returns a list of blocks, each with manifest (schema key), name, block_type, and short_description. Use ``workflow_blocks_get_schema``…

workflow_blocks_get_schema

Get the full schema of a specific Workflow block. Returns all properties, required fields, and descriptions for a block identified by its manifest name (as returned by ``workflow_blocks_list``).

workflow_specs_validate

Validate a Workflow JSON definition without executing it. Check whether a workflow definition is syntactically and semantically correct before saving or running it. Example workflow definition — det…

workflows_run

Execute a saved Workflow on one or more images. Runs a previously created Workflow against the provided images on the Roboflow serverless infrastructure. This always hits the latest **published** ver…

workflow_specs_run

Execute a Workflow from an inline JSON definition. Unlike ``workflows_run`` which runs a saved workflow by ID, this tool accepts a full workflow JSON spec and executes it directly. Useful for testing…

devices_list

List devices registered in the workspace. Returns a list of Device objects with id, name, status, last_heartbeat, platform/hardware info, tags, and created_at. ``status`` is ``online`` if a heartbeat…

devices_create

Provision a new v2 device. Requires the ``device:update`` scope. Returns ``{deviceId, installId, offlineProvisioningQrPayload?}``: ``installId`` is the short-lived token used by the device installer …

devices_get

Get a single device by id. Returns the Device object. 404 if the device does not exist or belongs to a different workspace. When inspecting a device, also call ``devices_get_config`` for its running…

devices_get_config

Get the device's current runtime configuration. Returns the full RFDM config JSON: ``device_id``, ``device_name``, ``workspace_id``, ``version``, ``last_updated``, ``config``, ``services``. The respo…

devices_get_default_config

Get the workspace's default device configuration. Returns ``{config, patch}``: ``config`` is the base default config with the workspace's ``config_patch`` applied (the template used when provisioning…

devices_update_config

Update the device's runtime configuration. Requires the ``device:update`` scope. The merge is a Firestore shallow update: omitted top-level fields stay as-is, but any top-level field you supply repla…

devices_get_config_history

List prior configuration revisions, newest first. Returns ``{data: ConfigRevision[], pagination: {next_cursor, has_more, limit}}``. Each ConfigRevision carries ``revision_id``, ``created_at``, and ``…

devices_streams_list

List streams configured on the device. Returns a list of Stream objects. Credential-bearing fields (URL userinfo, ``password``, ``api_key``, etc.) are redacted from the ``source`` field.

devices_streams_get

Get a single stream by id. Returns the Stream object. 404 if the stream does not exist on the device.

devices_get_logs

Fetch device logs from Elasticsearch. Rate-limited at two tiers: 5 requests / minute per IP, and 50 requests / minute globally across all callers (Elasticsearch protection). Returns ``{data: LogEntr…

devices_get_telemetry

Get aggregated hardware metrics (cpu, memory, disk, gpu). Rate-limited per-device: 60 requests / minute steady, plus a burst limit of 10 requests / 10 seconds. Returns ``{time_period, bucket_interva…

devices_get_events

List device and stream lifecycle events. Returns ``{data: Event[], pagination: {next_cursor, prev_cursor, has_more, limit}}``.

universe_search

Search Roboflow Universe for datasets or models. The query supports operators mixed with free-text: - Add 'model' to only return datasets with trained models - 'class:helmet,person' filters by class …

async_tasks_get

Poll an async task by id. Poll every 5 seconds; processing may take up to 30 seconds to start. Returns `{ taskId, status, progress, result?, error? }`. `status` is one of `created`, `running`, `compl…

meta_feedback_send

Report a bug, missing feature, UX friction, or documentation issue. Call this proactively when you encounter errors using Roboflow tools, when the user expresses frustration, when a tool is missing f…

create_project_app

Open a Prefab UI so the human can create a Roboflow project (calls ``projects_create`` when they confirm). Use this when project settings are unknown, debatable, or should be chosen or reviewed in a …

universe_search_app

Open a Prefab Universe UI for search, visual comparison, image skim, and fork—human picks the dataset. Use this when the next step needs human judgment or visible UX: exploring queries, comparing pub…

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How to use

Add to your Claude Desktop / Cursor / Cline MCP config:

{
  "mcpServers": {
    "roboflow_(official)": {
      "url": "https://mcp.roboflow.com/mcp",
      "transport": "streamable-http"
    }
  }
}