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Scale ai

Scale AI provides data labeling and annotation services for machine learning, offering access to a global workforce and sophisticated tools for training AI models with high-quality labeled data

Verdict

Scale AI's MCP lets your team create and manage data labeling tasks directly from Switchy. @mention it to spin up annotation jobs for images, documents, LiDAR point clouds, or text — then track progress and retrieve results without leaving your workspace. Most useful for ML teams preparing training data, product teams validating model outputs, or ops teams routing transcription work. You'll need a Scale AI account with API access; tasks you create bill against your Scale plan, and turnaround depends on your project's workforce settings.

Common use cases

Label training images for computer vision models
Transcribe invoices and receipts into structured data
Annotate LiDAR scans for autonomous vehicle datasets
Extract named entities from customer support transcripts
Batch-tag tasks by priority or data source

Integration

Vendor: Scale ai
Category: other
Auth: API_KEY
Tools: 41
Composio slug: scale_ai

Tools

Add Studio Assignments
Tool to add project assignments to team members in Scale AI Studio. Use when you need to assign specific projects to team members by their email addresses. This action creates new assignments for the specified team members and projects.
Add Task Tags
Tool to add tags to an existing task. Use when you need to tag or categorize tasks for organization and filtering. Automatically avoids duplicate tags.
Create Batch
Tool to create a new batch within a project. Use when you need to group multiple tasks together for organizational and processing purposes.
Create Document Transcription Task
Tool to create a document transcription task where workers transcribe and annotate information from single or multi-page documents. Use when you need to extract structured data from documents like invoices, forms, or screenshots.
Create Image Annotation Task
Tool to create an image annotation task where annotators label images with vector geometric shapes (box, polygon, line, point, cuboid, ellipse). Use when you need to annotate objects in images with bounding boxes, polygons, or other geometr
Create Lidar Annotation Task
Tool to create a lidar annotation task where annotators mark objects with 3D cuboids in 3D space. Use when you need to annotate LIDAR frame sequences with 3D object detection.
Create LiDAR Segmentation Task
Tool to create a LiDAR segmentation task where annotators assign semantic class labels to individual LiDAR points. Use when you need to annotate point cloud data with object classes such as vehicles, pedestrians, roads, buildings, etc. Eith
Create Named Entity Recognition Task
Tool to create a named entity recognition task for labelers to highlight text entity mentions. Use when you need to extract and label entities such as people, organizations, or locations from text.
Create Segmentation Annotation Task
Tool to create a segmentation task where annotators classify pixels in an image according to provided labels. Use when you need pixel-wise semantic segmentation of images.
Create Text Collection Task
Tool to create a textcollection task for collecting information from attachments and/or web sources. Use when you need to gather structured data from documents, websites, images, or other content by having taskers fill out defined fields.
Create Video Annotation Task
Tool to create a video annotation task where annotators draw geometric shapes around specified objects across video frames. Use when you need to annotate video content with bounding boxes, polygons, lines, points, cuboids, or ellipses. Acce
Create Video Playback Annotation Task
Tool to create a video playback annotation task where annotators draw shapes around specified objects in video files. Use when you need to annotate videos with bounding boxes, polygons, lines, points, cuboids, or ellipses for object detecti
Delete Task Tags
destructive
Tool to remove specified tags from a Scale AI task. Use when you need to clean up or modify task tags.
Delete Task Unique ID
destructive
Tool to remove the unique identifier from a task. Use when you need to remove a task's unique identifier for enhanced data management control.
Finalize Batch
Tool to finalize a batch so its tasks can be worked on. Use when you need to finalize a batch for Scale Rapid and Studio customers. For other customer types, this endpoint returns success without performing any action.
Get Assets
Tool to retrieve file assets with filtering capabilities by project and metadata. Use when you need to list or search for files uploaded to Scale AI, filtered by project and optionally by metadata. Supports cursor-based pagination for large
Get Batch
Tool to retrieve the details of a batch with the specified name. Use when you need to check the status or configuration of an existing batch.
Get Batch Status
Tool to retrieve the current status of a batch and task completion counts. Use when you need to monitor batch progress or check how many tasks are pending or completed.
Get Fixless Audits
Tool to retrieve fixless audits by task ID or audit ID. Use when you need to fetch audit information for quality assessment. At least one of task_id or id must be provided.
Get Project
Tool to retrieve details about a specific Scale AI project using its unique identifier. Use when you need to get project metadata including type, name, parameter history, and creation timestamp.
Get Quality Labelers
Tool to retrieve training attempts matching provided filter parameters. Use when you need to assess labeler performance and understanding of task instructions. At least one of quality_task_ids or labeler_emails must be provided.
Get Secure Task Response URL
Tool to retrieve secure authenticated task response data. Use when you need to access stored response data for 2D segmentation, video, and lidar tasks that cannot be included in the task JSON.
Get Studio Assignments
Tool to retrieve current project assignments of all active team users in Scale AI Studio. Use when you need to view team member assignments and workload distribution. Excludes invited or disabled team members.
Get Studio Batches
Tool to retrieve basic information about all pending batches in Studio. Use when you need to list batches organized by priority level.
Get Task
Tool to retrieve detailed information about a specific task in Scale AI. Use when you need to check task status, review task parameters, or access task results.
Get Task by ID
Tool to retrieve detailed information about a specific task using its task ID. Use when you need to check task status, retrieve results, or analyze task metadata.
Get Teams
Tool to retrieve basic information about all team members associated with the account. Use when you need to list team members, check roles, or view notification preferences.
Import File
Tool to import files from an external URL endpoint into Scale's system rather than uploading directly from local storage. Use when you need to import files from remote URLs for Scale AI projects or data labeling tasks.
Invite Team Member
Tool to invite users by email to team with specified role. Use when you need to add new team members with roles like labeler, member, or manager.
List Batches
Tool to retrieve all batches in descending order by creation date. Use when you need to list batches with optional filtering by project, status, or time range. Supports pagination via limit and offset parameters.
List Projects
Tool to retrieve information for all projects in the Scale AI account with optional archived filtering. Use when you need to browse or manage project metadata. Returns project details including type, name, parameter history, and creation ti
List Tasks
Tool to retrieve a paginated list of tasks in descending order by creation time. Use when you need to browse tasks with optional filtering by status, type, project, batch, tags, timestamps, or unique identifiers. Supports pagination via lim
Remove Studio Assignments
destructive
Tool to unassign projects from specified team members in Scale AI Studio. Use when you need to remove project assignments from one or more team members.
Re-send Task Callback
Tool to re-send a callback for a completed or errored task to the callback_url. Use when you need to manually trigger a callback resend for a task that has already been processed.
Reset Batch Priorities
Tool to restore batch priority order to default order (calibration batches first, then sorted by creation date). Use when you need to reset custom batch priorities back to the default ordering.
Set Batch Priorities
Tool to modify batch priority order in Scale AI Studio. Use when you need to adjust the priority order of pending batches. You must include all pending studio batches in the request.
Set Project Ontology
Tool to set ontologies on a Scale AI project. Ontologies define the labels or classes that tasks will reference, and projects maintain complete history of ontology versions. Use when you need to configure or update the classification labels
Set Project Parameters
Tool to set default parameters for tasks created under a project. Use when you need to establish or update default parameters that apply to future tasks unless overridden.
Set Task Metadata
Tool to set key-value metadata on an existing Scale AI task. Use when you need to attach custom metadata to track or organize tasks. This operation is idempotent.
Update Task Unique ID
Tool to update or assign a unique identifier to a task. Use when you need to set a custom identifier for task tracking in your system.
Upload File
Tool to upload a local file to Scale's servers with a maximum size limit of 80 MB per file. Use when you need to upload files for Scale AI projects or data labeling tasks.

Setup

Setup guide

11. In Switchy, open your workspace settings and navigate to the Integrations tab. 2. Search for Scale AI and click Connect. 3. You'll be prompted to paste your Scale API key — find this in your Scale dashboard under Settings > API Keys. 4. Grant the connection read and write access to tasks, batches, and projects. 5. Click Save to complete the connection. 6. Open any Space and type '@Scale AI list my projects' to confirm the integration responds with your active projects. 7. To create a task, @mention Scale AI and describe the job — for example, '@Scale AI create an image annotation task for this batch of product photos, use bounding boxes for defect detection.' The MCP will prompt you for required parameters like project ID and attachment URLs, then return a task ID you can reference later.

What teammates see: by default, memories from Scale ai are scoped to the Space (PROJECT visibility) - you can mark any memory PRIVATE or share it ORG-wide.

Works well with

Top models

Compatibility data appears once enough Spaces have used this MCP together with a given model.

How Switchy teams use it

Not enough Spaces yet to publish anonymised usage stats (we require ≥ 50 Spaces per week).

Starter prompts

Create Annotation Job

@Scale AI create an image annotation task for the 50 product images in my S3 bucket, use bounding box geometry to mark defects, assign to my default annotation team

Open in a Space →

Check Task Status

@Scale AI show me the status of task ID abc123, include how many documents are completed and any pending reviews

Open in a Space →

Batch Document Transcription

@Scale AI create a document transcription batch for these 20 invoice PDFs, extract vendor name, line items, and total amount into structured fields

Open in a Space →

Tag Tasks by Priority

@Scale AI add the tag 'high-priority' to tasks xyz789, xyz790, and xyz791 in my active project

Open in a Space →

Assign Project to Teammate

@Scale AI add alice@example.com to the 'Q1 Product Labeling' project in Studio so she can review annotations

Open in a Space →

Example outputs

Illustrative - representative of the model's voice and quality, not literal recordings.

Prompt

@scaleai show me all pending image annotation tasks in the 'product-catalog-2024' project that still need review

Output

Found 47 pending image annotation tasks in product-catalog-2024. The tasks are distributed across 3 batches: 'winter-collection' (18 tasks), 'spring-preview' (22 tasks), and 'accessories' (7 tasks). Most common tags: 'needs-qc', 'high-priority'. Average completion rate across batches is 68%. 12 tasks have been flagged for quality review by annotators. This is an illustrative example — actual task counts and batch names will reflect your project's current state.

Notes

This demonstrates Scale's task-querying capabilities for managing annotation workflows. Requires API key with read access to the specified project. Response times depend on project size; projects with 10,000+ tasks may take several seconds to filter and return results.

Prompt

@scaleai create a new batch called 'q1-vehicle-detection' in our autonomous-driving project and add 200 lidar annotation tasks for the sensor data we uploaded yesterday

Output

Created batch 'q1-vehicle-detection' (ID: batch_abc123) in project autonomous-driving. Queued 200 LiDAR annotation tasks for 3D cuboid detection. Tasks configured with your project's default instruction set and will be distributed to Scale's annotator pool based on your SLA tier. Estimated completion: 48-72 hours depending on queue depth. This is an illustrative example — actual batch IDs and completion times vary by account tier and current platform load.

Notes

Shows Scale's batch creation and task submission workflow for computer vision projects. This is a write operation that incurs annotation costs per task. Ensure your uploaded sensor data URLs are accessible to Scale's workers and that your account has sufficient credits before bulk task creation.

Prompt

@scaleai analyze the quality metrics across all completed named entity recognition tasks from last week and tell me which annotator teams had the highest agreement scores

Output

Analyzed 1,847 completed NER tasks from the past 7 days. Team 'linguistics-specialists' achieved 94.2% inter-annotator agreement (IAA), followed by 'general-nlp' at 89.7%. Most common entity types labeled: PERSON (3,201 mentions), ORG (2,456), LOCATION (1,893). 23 tasks were flagged for consensus review due to low agreement (<80%). Team 'linguistics-specialists' also had the fastest average completion time at 4.2 minutes per task. This is an illustrative example based on typical NER project patterns.

Notes

Demonstrates combining Scale's task metadata with AI reasoning to surface quality insights. Scale's API returns raw task and annotation data; the LLM synthesizes agreement scores and team performance. Accuracy depends on whether your project has consensus/review workflows enabled and sufficient task volume for statistical significance.

Use-case deep-dives

Training data pipeline for ML team

When Scale AI fits a 3-person ML team building custom models

A three-person ML team at a logistics startup needs 10,000 labeled images of warehouse pallets to train a damage-detection model. Scale AI is the right call here because the team can spin up an Image Annotation Task with bounding boxes, define the label taxonomy once, and let Scale's workforce handle the tedious labeling work while the engineers focus on model architecture. The Create Batch tool keeps the data organized by shipment date, and tagging lets them filter edge cases for model retraining. The threshold: if your labeling budget is under $2,000 or you need real-time feedback loops with annotators, Scale's API-only workflow and per-task pricing might feel too rigid. For teams shipping a model in 4-6 weeks and needing professional-grade labels at scale, this MCP turns annotation from a bottleneck into a background task.

Document extraction for compliance team

Scale AI for invoice processing at mid-sized finance ops

A six-person accounts payable team at a mid-market SaaS company processes 400 invoices a week from vendors with inconsistent formats—PDFs, scanned receipts, email attachments. The Create Document Transcription Task tool is built for exactly this: you define the fields you need (vendor name, total, line items), upload the document batch, and Scale's workers extract structured JSON you can pipe straight into your ERP. The Named Entity Recognition task handles edge cases where invoice language varies by region. The trade-off: if your invoice volume is under 50 a week or your formats are already standardized, you're better off with a simpler OCR tool. Scale AI wins when document variety is high, accuracy requirements are strict, and you'd rather pay per task than build an in-house annotation team.

Autonomous vehicle dataset prep

When Scale's LiDAR tools justify the API key for robotics teams

A 12-person robotics team building a warehouse navigation system has 200 hours of LiDAR point cloud data from their test fleet. The Create Lidar Annotation Task and Create LiDAR Segmentation Task tools are purpose-built for this workflow: annotators mark 3D cuboids around forklifts and pallets, then assign semantic labels to every point in the cloud. Scale's workforce knows the difference between a static obstacle and a moving vehicle, which matters when your robot's safety depends on it. The Studio Assignments tool lets you route tricky edge cases to senior annotators. The boundary: if your LiDAR dataset is under 20 hours or your object classes are simple (just 'obstacle' vs 'floor'), the per-task cost and API overhead aren't worth it. For teams with complex 3D data and a six-month timeline to production, Scale turns raw sensor dumps into training-ready datasets.

Frequently asked

What does the Scale AI MCP let me do in Switchy?

It lets you create and manage Scale AI annotation tasks — image bounding boxes, LiDAR cuboids, document transcription, named entity recognition — directly from Switchy's AI workspace. You can also batch tasks, assign projects to team members, and add tags for organization. It's built for teams that need labeled training data without leaving their workflow.

Do I need a Scale AI account to use this MCP?

Yes. You need an active Scale AI account and an API key with write permissions. The MCP authenticates via API key, so whoever connects it must have access to your Scale workspace and the authority to create tasks and batches. If you're on a team plan, check with your Scale admin before connecting.

Can the Scale AI MCP retrieve completed annotations or just create tasks?

The MCP focuses on task creation and project management — creating batches, assigning work, tagging tasks. It doesn't currently expose tools to fetch completed annotations or download labeled datasets. For retrieval, you'll still need to use Scale's dashboard or their REST API directly.

How is this different from using Scale's web dashboard?

The MCP lets you spin up annotation tasks from inside Switchy's AI chat, so you can brief an AI agent, generate task parameters, and push them to Scale without context-switching. The dashboard is still better for reviewing finished work or adjusting task templates. Think of the MCP as the input side of your labeling pipeline.

Who on my team should connect the Scale AI MCP?

Whoever manages your annotation projects and has API key access. Typically a machine learning engineer or data ops lead. Once connected, anyone in your Switchy workspace can trigger task creation through AI agents, but the connection itself uses one person's Scale credentials.