Unitlab MCP Server
Upload images, ask for labels, and run data workflows from the AI assistant you already use.
Your workspace. Your permissions.
street-scene.jpgImage attachedUpload this image to my Road scenes project. Label the visible cars, people, and bicycles.
GET CONNECTED
Add Unitlab, sign in, and choose your workspace. Your assistant is ready to use the tools you allow.
/mcp in Claude Code and sign in to Unitlab.For every project, add --scope user to the command.
claude mcp add --transport http unitlab https://api.unitlab.ai/mcpOn Team and Enterprise, an owner adds the connector for the organization before members connect.
Official client guidehttps://api.unitlab.ai/mcpIf sign-in does not open, run codex mcp login unitlab.
codex mcp add unitlab --url https://api.unitlab.ai/mcpCustom MCP availability depends on your ChatGPT plan and workspace policies.
Official client guidehttps://api.unitlab.ai/mcpUse the project’s .cursor/mcp.json for a project-specific connection.
{
"mcpServers": {
"unitlab": {
"url": "https://api.unitlab.ai/mcp"
}
}
}/mcp auth unitlab inside Gemini CLI.Add --scope user to make the server available across projects.
gemini mcp add --transport http unitlab https://api.unitlab.ai/mcpUse an MCP-enabled chat mode in VS Code. Client and organization policies still apply.
Official client guidecode --add-mcp "{\"name\":\"unitlab\",\"type\":\"http\",\"url\":\"https://api.unitlab.ai/mcp\"}"For another Unitlab deployment, copy its address from Settings → Connected assistants.
https://api.unitlab.ai/mcpPrefer to start in Unitlab? Open Settings → Connected assistants → Connect assistant.
Open UnitlabTHE TOOLKIT
From the first upload to the next dataset release. Your assistant calls these tools within your role and connection permissions.
| Category | What your agent can do | Tools |
|---|---|---|
| Getting started | Understand your connection, get practical guidance, and diagnose blockers. get_contextdiagnoseget_help | 3 |
| Projects & ontologies | Create projects, define classes and properties, and manage ontology versions. list_ontologiesget_ontologycreate_ontologyupdate_ontologyset_ontology_versionget_instructionslist_projectsget_projectcreate_projectupdate_project | 10 |
| Data & curation | Upload, import, organize, group, tag, and version your data. list_assetsprepare_uploadcomplete_uploadget_upload_statusattach_datadetach_datagroup_assetslist_itemsupdate_itemscreate_tagupdate_assetscreate_datasetupdate_datasetupdate_folderupdate_collectionstart_cloud_import | 16 |
| Content & annotation | Read source content, inspect annotation schemas, save labels, and paint LiDAR points. get_annotation_schemaget_annotationsvalidate_annotationssave_annotationsread_contentpaint_points | 6 |
| AI-assisted labeling | Detect, segment, refine, find similar objects, and track supported objects. segment_objectdetect_objectsfind_similar_objectsrefine_objectstrack_object | 5 |
| Models & examples | Discover models, manage reference examples, and follow labeling runs. list_modelsget_model_runstart_model_runcancel_model_runget_referencessave_reference_examplesdelete_reference_objectcreate_modelupdate_model | 9 |
| Tasks & workflows | Claim and assign tasks, configure workflow stages, and move work forward. list_tasksget_taskget_next_taskget_groupclaim_taskassign_tasksrelease_taskget_workflowconfigure_workflowperform_task_actionmove_tasks | 11 |
| Quality & consensus | Review Consensus results, validation issues, and benchmark scores. claim_consensus_taskget_consensus_tasksubmit_consensus_voteget_consensus_reviewset_consensus_approvalfinish_consensus_reviewlist_gold_labelsupdate_gold_labelget_gold_resultsget_quality_issuesget_consensus_report | 11 |
| Comments & notifications | Keep comments, issues, and notifications connected to the work. list_commentsadd_commentresolve_commentlist_issueslist_notificationsmark_notifications_read | 6 |
| Releases & exports | Create, monitor, download, clone, and manage dataset releases. list_releasesget_releaseretry_releasecreate_releaseget_release_downloadclone_releasedelete_releaseupdate_release | 8 |
| Members & roles | Manage workspace membership and roles when authorized. list_membersupdate_memberlist_rolescreate_roleupdate_role | 5 |
| Administration & statistics | Manage integrations and API keys, inspect audit events, and read statistics. create_api_keyupdate_api_keylist_cloud_storagescreate_cloud_storageupdate_cloud_storagelist_audit_eventsget_statistics | 7 |
No matching tools. Try a different name or category.
Need a hand? get_help explains workflows and tools. diagnose checks what is blocking your work.
PUT IT TO WORK
Copy an example to get started. Your assistant works with the same projects, labels, and review stages your team uses in Unitlab.
Find your projects, inspect the context, and see the annotation work assigned to you.
Explore data curationShow my Unitlab projects and summarize the annotation work assigned to me.
Read access
Create labels with your classes, run supported AI tools, and move work through annotation and review.
Explore annotationIn my image project, use the Car class to detect visible cars on the next task. Save the labels and show them for review.
Write access · AI usage
Inspect blockers, create dataset releases, and retrieve exports in the formats your project supports.
Explore dataset managementCheck whether my project is ready for a release. Show any blockers and the supported export formats before creating it.
Read first · Write to create
ACCESS YOU CONTROL
Connect as yourself. Choose one workspace and decide what your assistant can do.
Manage in UnitlabExplore with Read only, or allow changes with Read and write. Your role and workspace access still apply.
Customize access during sign-in or edit your own connection later in Connected assistants.
Disconnect in Unitlab at any time. Reconnect to change the workspace or access level.
Some actions save changes or start jobs immediately. Some AI operations and model stages use your workspace’s AI quota.
The Unitlab MCP server connects compatible AI assistants to your Unitlab workspace through Model Context Protocol. It exposes tools for data management, annotation, AI-assisted labeling, tasks, review, workflows, and dataset releases. Your assistant calls those tools using the workspace and permissions you authorize.
Unitlab provides setup instructions for Claude Code, Claude web and desktop, Codex, ChatGPT, Cursor, Gemini CLI, and VS Code. Other clients need support for remote MCP over Streamable HTTP and OAuth. The available interface and connection options depend on the client version, plan, and workspace policy.
Add https://api.unitlab.ai/mcp in your assistant, then complete Unitlab sign-in. Select a workspace, choose Read only or Read and write, customize the permissions if needed, and click Allow. This OAuth connection does not require you to create or paste an API key. Unitlab also provides setup instructions under Settings → Connected assistants.
The MCP tools support image, video, LiDAR/3D, medical, pathology, geospatial, audio, text, document, HTML, sensor, and tabular data. Grouped multimodal assets are supported too. Reading, annotation types, AI operations, and exports vary by modality. The assistant can request the item’s annotation schema and built-in data-type guidance before making changes.
Yes, with a Read and write connection, the required role permissions, and applicable workspace access. Tools can save annotations, modify workflows, and start model runs. Some AI-assisted operations and model stages consume the workspace’s AI quota. Choose Read only if you want exploration without writes; approval behavior inside the assistant depends on that client.
Each connection acts as the member who authorized it in one selected workspace. Your role and any permissions you turn off limit the available tools. In Settings → Connected assistants, use Edit permissions for your own connection or Disconnect to revoke access. To select another workspace or read/write access level, reconnect and approve the new settings.
MCP adds another way to work with Unitlab. Your team can continue reviewing and refining the same data in Unitlab’s annotation interface. The assistant can read annotated previews, check validation problems, and follow task and review stages. Generated labels still need the review appropriate to your project.
Ask the assistant to check its connection context and call Unitlab’s built-in help. The get_help tool explains workflows, tools, data types, and errors; diagnose checks blockers on a project, item, or task. A missing permission, unavailable workspace access, unsupported operation, or exhausted quota can limit what a connection can do.
UNITLAB MCP SERVER
Connect your AI assistant to manage datasets, annotate objects, and prepare releases with the tools and permissions you choose.
