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 Google ADK 10 tools — connect in under 2 minutes

Name: Relevance AI
Availability: InStock
Author: Vinkius

google.github.io/adk-docs/tools/mcp-tools

Built by Vinkius GDPR 10 Tools SDK

Google Agent Development Kit (ADK) is Google's framework for building production AI agents. Add Relevance AI as an MCP tool provider through Vinkius and your ADK agents can call every tool with full schema introspection.

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

from google.adk.agents import Agent
from google.adk.tools.mcp_tool import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import (
    StreamableHTTPConnectionParams,
)

# Your Vinkius token. get it at cloud.vinkius.com
mcp_tools = McpToolset(
    connection_params=StreamableHTTPConnectionParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    )
)

agent = Agent(
    model="gemini-2.5-pro",
    name="relevance_ai_agent",
    instruction=(
        "You help users interact with Relevance AI "
        "using 10 available tools."
    ),
    tools=[mcp_tools],
)

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.

Google ADK natively supports Relevance AI as an MCP tool provider. declare Vinkius Edge URL and the framework handles discovery, validation, and execution automatically. Combine 10 tools with Gemini's long-context reasoning for complex multi-tool workflows, with production-ready session management and evaluation built in.

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 Google ADK 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 Google ADK via MCP

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

Install Google ADK

Run pip install google-adk

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Create the agent

Save the code above and integrate into your ADK workflow

Explore tools

The agent will discover 10 tools from Relevance AI via MCP

Why Use Google ADK with the Relevance AI MCP Server

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

Google ADK natively supports MCP tool servers. declare a tool provider and the framework handles discovery, validation, and execution

Built on Gemini models, ADK provides long-context reasoning ideal for complex multi-tool workflows with Relevance AI

Production-ready features like session management, evaluation, and deployment come built-in. not bolted on

Seamless integration with Google Cloud services means you can combine Relevance AI tools with BigQuery, Vertex AI, and Cloud Functions

Relevance AI + Google ADK Use Cases

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

Enterprise data agents: ADK agents query Relevance AI and cross-reference results with internal databases for comprehensive analysis

Multi-modal workflows: combine Relevance AI tool responses with Gemini's vision and language capabilities in a single agent

Automated compliance checks: schedule ADK agents to query Relevance AI regularly and flag policy violations or configuration drift

Internal tool platforms: build self-service agent platforms where teams connect their own MCP servers including Relevance AI

Relevance AI MCP Tools for Google ADK (10)

These 10 tools become available when you connect Relevance AI to Google ADK 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 Google ADK

Ready-to-use prompts you can give your Google ADK 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 Google ADK

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

McpToolset not found

Update: pip install --upgrade google-adk

Relevance AI + Google ADK FAQ

Common questions about integrating Relevance AI MCP Server with Google ADK.

How does Google ADK connect to MCP servers?

Import the MCP toolset class and pass the server URL. ADK discovers and registers all tools automatically, making them available to your agent's tool-use loop.

Can ADK agents use multiple MCP servers?

Yes. Declare multiple MCP tool providers in your agent configuration. ADK merges all tool schemas and the agent can call tools from any server in a single turn.

Which Gemini models work best with MCP tools?

Gemini 2.0 Flash and Pro models both support function calling required for MCP tools. Flash is recommended for latency-sensitive use cases, Pro for complex reasoning.