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MCP Integrations Relevance Ai
MCP & AI Agent Integration

Relevance Ai
automation.

13 AI agent actions for Relevance Ai — callable from any MCP-compatible runtime, Claude, Cursor, or Cerebral OS workflow. Use Relevance Ai to organize files, generate documents, and manage permissions programmatically. Full governance, audit trail, and dry-run safety on every execution.

Connect Relevance Ai free Browse all integrations →
No credit card required
Live in production environments
<200ms median execution
Dry-run before production
Execution trace
live
13
actions
100%
governed
<200ms
latency
13
AI agent actions
6
Read operations
7
Write operations
2
High-risk actions (approval gated)
Files & Documents Relevance Ai is a Files & Documents integration — use it to automate file storage, sharing, and document management from any AI agent or MCP-compatible runtime.
Actions

What you can do
with Relevance Ai.

Every action below is available as an MCP tool and a verb in Cerebral OS — callable from any AI agent, Claude, Cursor, Windsurf, or your own runtime via the BYOA API. All executions are governed, audited, and dry-run safe.

Get Dataset
relevance_ai:get_dataset
Fetch a single dataset by ID with schema and metadata information.
Read Low risk
Get Document
relevance_ai:get_document
Retrieve a single document by ID from a dataset.
Read Low risk
Get Job Status
relevance_ai:get_job_status
Check the status of an asynchronous job (workflow run, vectorization, etc.).
Read Low risk
List Datasets
relevance_ai:list_datasets
List all datasets with optional filtering and pagination.
Read Low risk
List Workflows
relevance_ai:list_workflows
List all available workflows with optional filtering and pagination.
Read Low risk
Search Documents
relevance_ai:search_documents
Search documents in a dataset using text or vector similarity.
Read Low risk
Create Dataset
relevance_ai:create_dataset
Create a new dataset with optional schema definition.
Write Medium risk
Create Vector Field
relevance_ai:create_vector_field
Create a vector field in a dataset by vectorizing text from an existing field.
Write Medium risk
Delete Dataset
relevance_ai:delete_dataset
Delete a dataset permanently. This action cannot be undone.
Write High risk
Delete Document
relevance_ai:delete_document
Delete a document from a dataset permanently. This action cannot be undone.
Write High risk
Insert Documents
relevance_ai:insert_documents
Insert documents into a dataset. Documents will be validated against the dataset schema.
Write Medium risk
Run Workflow
relevance_ai:run_workflow
Execute a workflow with optional input parameters.
Write Medium risk
Update Document
relevance_ai:update_document
Update an existing document in a dataset with new field values.
Write Medium risk
MCP & Runtime API

Call Relevance Ai
from any AI agent.

Any AI agent — Claude, Cursor, LangChain, AutoGen, or your own — can call Relevance Ai actions through the Cerebral OS Runtime API. Governance, credentials, and audit trail fire automatically.

relevance_ai:get_dataset READ
# Call via Runtime API
curl
-X POST \
  "https://api.cerebralos.com/v1/runtime/actions/run"
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "verb": "relevance_ai:get_dataset",
    "args": {},
    "execution_id": "agent-001"
  }'
relevance_ai:create_dataset WRITE
# Dry-run first — no production risk
curl
-X POST \
  "https://api.cerebralos.com/v1/runtime/actions/run"
  -H "X-API-Key: YOUR_KEY" \
  -d '{
    "verb": "relevance_ai:create_dataset",
    "args": {},
    "execution_id": "agent-001",
    "metadata": {"dryRun": true}
  }'
Get your Runtime API key at app.cerebralos.com/signup — 1,000 free executions, no credit card required.
AI agent examples

What your AI agent
can do with Relevance Ai.

Real patterns your AI agent can execute via MCP or the Runtime API. Every action governed, dry-run safe, and fully audited.

Trigger
AI agent needs Relevance Ai data
Call relevance_ai:get_dataset via MCP or Runtime API
AI processes result and takes next action
Full execution logged to audit trail automatically
Trigger
Workflow needs to write to Relevance Ai
Dry-run validates relevance_ai:create_dataset before execution
Approval gate fires if risk level is high
Action executes with full governance — logged, audited, reversible
Trigger
File or document event
Process with AI
Route for approval
Notify team
How it works

Every Relevance Ai action
governed end-to-end.

Cerebral OS isn't a connector. It's the execution layer that sits in front of Relevance Ai — adding governance, dry-run safety, and a full audit trail to every operation.

Governance first
Every verb carries a risk classification. High-risk writes require explicit approval gates before they execute in production.
Dry-run safe
Simulate any Relevance Ai action before it touches production. See exactly what would happen before a single real call is made.
Immutable audit trail
Every Relevance Ai action is logged — what ran, what changed, who approved it, when it happened. Full history on every verb, forever.
Connect with

Relevance Ai works best
alongside these.

Build multi-step workflows that connect Relevance Ai to the rest of your stack. All governed. All audited.

Google Drive Dropbox Box Notion Slack
Relevance Ai integration

Start free.
No credit card required.

Start free with 1,000 runs — no credit card required. Connect Relevance Ai in minutes, dry-run every action before it touches production, full audit trail on everything.

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