Can the agent monitor a long-running relevance AI agent task?

Yes. You can trigger an agent using `trigger_agent`, and because it provides a `run_id`, you can explicitly prompt your local Assistant to periodically "check in on the status using `get_agent_run` every minute until finished" or ask it to summarize the step-by-step agent logs after completion.

What is the differences between tasks, tools, and agents in Relevance AI?

Agents are autonomous workers capable of making step-by-step reasoning choices based on instructions and tools. Tasks are linear, pre-chained sets of commands and prompts. Tools (`list_tools`) are the individual capabilities, like a custom API integration or web scraper, that tasks and agents utilize to perform their actions.

How do I find my specific Region and Project ID?

These details are typically nested within the URL string when you are logged into your workspace or found globally in your developer API keys configuration pane inside your Relevance AI team dashboard. The Region is usually something like 'us-east-1' or 'v2'.

Relevance AI MCP Server for AutoGen 10 tools — connect in under 2 minutes

Name: Relevance AI
Availability: InStock
Author: Vinkius

microsoft.github.io/autogen/docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools Framework

Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Relevance AI as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with McpWorkbench(
        server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
        transport="streamable_http",
    ) as workbench:
        tools = await workbench.list_tools()
        agent = AssistantAgent(
            name="relevance_ai_agent",
            tools=tools,
            system_message=(
                "You help users with Relevance AI. "
                "10 tools available."
            ),
        )
        print(f"Agent ready with {len(tools)} tools")

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About Relevance AI MCP Server

Connect your conversational AI to your Relevance AI workspace. By wrapping your custom agents, datasets, and API tools into this MCP extension, you transform your chat interface into a command center for orchestrating complex, autonomous AI operations and large-scale data workflows.

AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Relevance AI tools. Connect 10 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.

What you can do

Orchestrate Agents — Command your pre-built autonomous agents to execute tasks (trigger_agent). Monitor their progress and read their exact reasoning steps dynamically (get_agent_run). Use list_agents to discover all available AI worker configurations.
Execute Tasks & Workflows — Trigger predefined chained prompts or specific micro-tasks without leaving your chat (trigger_task), scaling repetitive workflows reliably.
Manage Knowledge Datasets — Take full control of your vector databases and tables. Insert new rows of knowledge directly from conversational context (insert_documents), retrieve raw unstructured data entries (get_documents), or surgically delete obsolete knowledge base items (delete_documents).

The Relevance AI MCP Server exposes 10 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Relevance AI to AutoGen via MCP

Follow these steps to integrate the Relevance AI MCP Server with AutoGen.

Install AutoGen

Run pip install "autogen-ext[mcp]"

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Integrate into workflow

Use the agent in your AutoGen multi-agent orchestration

Explore tools

The workbench discovers 10 tools from Relevance AI automatically

Why Use AutoGen with the Relevance AI MCP Server

AutoGen provides unique advantages when paired with Relevance AI through the Model Context Protocol.

Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Relevance AI tools to solve complex tasks

Role-based architecture lets you assign Relevance AI tool access to specific agents. a data analyst queries while a reviewer validates

Human-in-the-loop support: agents can pause for human approval before executing sensitive Relevance AI tool calls

Code execution sandbox: AutoGen agents can write and run code that processes Relevance AI tool responses in an isolated environment

Relevance AI + AutoGen Use Cases

Practical scenarios where AutoGen combined with the Relevance AI MCP Server delivers measurable value.

Collaborative analysis: one agent queries Relevance AI while another validates results and a third generates the final report

Automated review pipelines: a researcher agent fetches data from Relevance AI, a critic agent evaluates quality, and a writer produces the output

Interactive planning: agents negotiate task allocation using Relevance AI data to make informed decisions about resource distribution

Code generation with live data: an AutoGen coder agent writes scripts that process Relevance AI responses in a sandboxed execution environment

Relevance AI MCP Tools for AutoGen (10)

These 10 tools become available when you connect Relevance AI to AutoGen via MCP:

delete_documents

This action is irreversible. Deletes documents from a dataset by their IDs

get_agent_run

Retrieves the status and logs of a specific agent run

get_documents

Retrieves documents from a dataset

insert_documents

Provide documents as a JSON array of objects. Inserts documents into a dataset

list_agents

Lists all AI agents in the Relevance AI studio

list_datasets

Lists all datasets (knowledge tables) in the project

list_tasks

Lists all tasks (chained prompts) in the studio

list_tools

Lists all custom tools registered in the studio

trigger_agent

Provide inputs as a JSON object. Triggers an AI agent execution

trigger_task

Triggers a specific task execution

Example Prompts for Relevance AI in AutoGen

Ready-to-use prompts you can give your AutoGen agent to start working with Relevance AI immediately.

"List all available agents in my Relevance AI Studio and their IDs."

"Start a run for the 'Market Analysis' agent passing `{"company": "OpenAI"}` as the payload, then tell me the Run ID."

"Insert this JSON array of top competitor articles into the 'competitor_docs' dataset."

Troubleshooting Relevance AI MCP Server with AutoGen

Common issues when connecting Relevance AI to AutoGen through the Vinkius, and how to resolve them.

McpWorkbench not found

Install: pip install "autogen-ext[mcp]"

Relevance AI + AutoGen FAQ

Common questions about integrating Relevance AI MCP Server with AutoGen.

How does AutoGen connect to MCP servers?

Create an MCP tool adapter and assign it to one or more agents in the group chat. AutoGen agents can then call Relevance AI tools during their conversation turns.

Can different agents have different MCP tool access?

Yes. AutoGen's role-based architecture lets you assign specific MCP tools to specific agents, so a querying agent has different capabilities than a reviewing agent.