Relevance AI MCP for AI. Control multi-agent teams and task history from chat.
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…and any MCP-compatible client
Connect to your AI in seconds.
Relevance AI MCP Server connects your preferred AI client (Claude, Cursor, etc.) directly to a professional agent orchestration platform. This server gives you tools to list, trigger, and monitor entire multi-agent teams—from initiating complex 'Studios' to checking task results in real time.
You get full control over autonomous AI workflows without leaving your chat window.
What your AI can do
Get knowledge
Gets specific details from a designated knowledge base used by the agents.
List executions
Retrieves detailed historical records for all past agent executions across the platform.
Delete task
Permanently removes a specific, completed task record from the system.
You retrieve a roster of every active agent persona running in your workspace, knowing exactly what capabilities you have available.
You start an entire multi-step task by calling a specific agent and providing it with initial inputs for processing.
You execute specialized, pre-built workflows—the 'Studios'—by passing complex parameters directly through the chat interface.
You get a real-time status update on any background task, whether it’s pending or finished, along with its final output data.
You list available knowledge bases or search through specific items within an agent's dedicated dataset using natural language queries.
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Relevance AI MCP Server: 11 Tools for Agent Management
Use these tools to list agents, run custom studios, check task history, and manage the entire lifecycle of your autonomous AI workflows.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using Relevance AI on VinkiusGet Knowledge
Gets specific details from a designated knowledge base used by the agents.
List Executions
Retrieves detailed historical records for all past agent executions across the...
Delete Task
Permanently removes a specific, completed task record from the system.
Get Agent Details
Retrieves metadata about an agent, showing its general configuration and...
Get Task Status
Checks the current status and final results of any background task run by an agent.
List Agents
Provides a list of all active AI agent personas available in the workspace.
List Knowledge Items
Lists specific, indexed items available within a knowledge base.
List Agent Tasks
Lists a summary of recent tasks completed or started by your autonomous agents.
List Tools
Enumerates all custom 'Studios' or tools that can be run by the agents.
Trigger Agent
Starts a new, defined task using a specified agent persona and its initial input...
Trigger Tool
Executes a specific custom tool (Studio) immediately with required parameters.
Security and governance baked right in.
Pick your AI client below to get set up. Just create a Vinkius account, subscribe, and you're instantly up and running. We handle the entire backend infrastructure, delivering out-of-the-box support for HTTPS Streamable, SSE, and OAuth2—zero messy routing required.
Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp
Replace [YOUR_TOKEN_HERE] with your token
from cloud.vinkius.com. For OAuth-protected servers, expand
Advanced settings to add credentials.
Start a conversation
Open a new chat. The Relevance AI integration is available immediately — no restart needed.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
- Import from OpenAPI, Swagger, or YAML specs
- Create Agent Skills with progressive disclosure
- Deploy to edge with MCPFusion framework
- Built in DLP, auth, and compliance on every call
- Real time usage dashboard and cost metering
- Publish to catalog or keep private
Make Your AI Do More
Start with Relevance AI, then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,100+ others, all in one place
- Add new capabilities to your AI anytime you want
- Every connection is secured and compliant automatically
- Track usage and costs across all your servers
- Works with Claude, ChatGPT, Cursor, and more
- New servers added to the catalog every week
Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Relevance AI. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 11 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Checking on AI tasks shouldn't require switching tabs.
Today, checking the status of a complex agent task means you leave your chat window, navigate to the dedicated dashboard, find the job ID in the history logs, and then refresh the page every five minutes until it finally shows 'Complete.' It’s tedious, distracting, and easily breaks context.
With this MCP Server, that whole process vanishes. You simply ask your agent client for the status using `get_task_status`. The system instantly reports whether the task is pending, if it succeeded, or if an error occurred—all without you leaving your conversation.
Use Relevance AI MCP Server: Manage Agent Workflows
Manual workflows often require chaining agents together with specific inputs (e.g., 'Agent A gets the data, then passes it to Agent B for formatting'). Doing this manually means copying outputs from one dashboard and pasting them as prompts into another.
This server lets you define and execute those chains programmatically. You use `trigger_agent` or `list_tools` to manage that full process flow directly through the chat interface, treating your agents like callable services.
What your AI can actually do with this
You connect your AI client straight into a professional agent orchestration platform, giving you full control over multi-agent teams without ever leaving your chat window. This server lets you manage complex autonomous workflows through simple calls to specific tools.
Getting Started and Discovery:
You need to know what’s running before you kick off anything big. You can pull a roster of every active AI agent persona available in the workspace by calling list_agents. If you want to see exactly what capabilities an agent has, use get_agent_details to retrieve its full metadata and configuration.
When it comes to custom workflows, you don't have to guess; run list_tools to enumerate every 'Studio' or specialized tool the agents can execute.
Executing Workflows and Tasks:
To get work done, you start tasks. You trigger an entire multi-step process by calling trigger_agent, giving a specific agent persona its initial inputs for processing. For highly specialized needs, you bypass the full workflow and run a custom tool instantly using trigger_tool with required parameters. These tools let you execute complex, pre-built workflows immediately.
Checking Status and History:
Once something starts running in the background, you need to track it down. Use get_task_status to get a real-time update on any task—whether it’s still pending or if it's done for good—and grab its final output data. To see a summary of everything that’s been started or finished by your agents, run list_agent_tasks.
For a deep dive into the platform's history, you can retrieve detailed historical records for all past agent runs using list_executions. If a task is complete and you need to clear up space, delete_task permanently removes specific, completed task records from the system.
Managing Knowledge and Data:
Your agents need data that isn't in their training set. To ground them in your company’s private knowledge, you can list available knowledge bases using list_knowledge_items. You also get specific details from a designated knowledge base by calling get_knowledge, allowing the AI to pull precise facts into its process.
This setup gives you an operations control panel built right into your chat. You manage everything—from initiating complex, multi-step agent teams to checking task results and deleting old records—all without jumping between separate dashboards or refreshing pages.
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Here's how it actually works
The bottom line is: Your AI client becomes a central, single point of control for managing all your complex, autonomous AI teams.
Subscribe to the server, then provide your Relevance AI Region Code and API Key credentials.
Your AI client sends a request (e.g., 'Show me all agents') which triggers one of the 11 listed tools on this MCP Server.
The tool executes the action—like listing tasks or triggering an agent—and sends the structured result back to your chat window.
Who is this actually for?
This is for the Ops Manager who gets tired of clicking through multiple dashboards just to check if an agent finished its task. It's for the Automation Engineer who needs to chain together several different agents via a natural conversation, and it’s for developers building systems that require real-time visibility into complex AI processes.
You monitor agent deployments by listing all available agents or checking the history of executions without leaving your chat interface.
You automate complex, multi-agent workflows by triggering specific agents and passing them dynamic inputs through conversation prompts.
You integrate real-time task results and custom studio execution into a larger business architecture using the structured data output from this server.
What Changes When You Connect
Task Lifecycle Visibility: Instead of guessing, use get_task_status to check if a background job is still running or if the final results are ready. You get immediate answers without refreshing dashboards.
Full Workforce Listing: The list_agents tool shows you every single agent persona available right now. This lets you know exactly which specialized worker you need for the job.
Complex Workflows on Demand: Use trigger_tool to run a specific 'Studio' with complex parameters, bypassing the standard conversational flow when precision is needed.
Historical Audit Trail: The list_executions tool gives you access to every past agent interaction. This is critical for debugging or reviewing billing reports later.
Knowledge Control: You don't just assume agents know things; use get_knowledge and list_knowledge_items to verify the exact data source they are reading from.
See it in action
Auditing a Failed Campaign
A campaign failed because an agent used bad data. Instead of digging through logs, you use list_executions to get the full history of runs and then use get_task_status on the specific job ID to see exactly which step failed and why.
Onboarding a New Agent Persona
You need to know what capabilities your team has. You start by calling list_agents to get all available personas, then use get_agent_details on the new one to confirm its specific parameters and inputs.
Running a Batch Analysis
You have 50 leads that need scoring. You don't want a conversation; you just want results. You call trigger_tool directly, passing the batch data payload to the 'Lead Qualifier Studio,' and get immediate status updates.
Cleaning Up Old Data
A task ran years ago that generated junk data. Before running a new job, you use delete_task with the old ID to permanently remove the record, keeping your history clean and accurate.
The honest tradeoffs
Treating Agents like Search Boxes
You ask the agent, 'What was my last task?' and wait for it to guess. The response is vague, giving no actionable ID or status.
Don't rely on conversation alone. Use list_agent_tasks first to get a list of recent IDs, then use get_task_status with the specific ID you need.
Overloading the Chat Context
Trying to pass 10 different parameters for an agent workflow in one massive text block. The AI client fails or only uses half the data.
For complex, structured inputs, use trigger_tool. This tool forces you to define and pass specific, required arguments like a function call.
Ignoring Tool Availability
Assuming an agent has access to the 'Sales Lead' knowledge base when it only knows about marketing data. The task fails silently or gives irrelevant results.
Always check what's available first by calling list_knowledge_items and confirming that your target dataset is visible and accessible.
When It Fits, When It Doesn't
Use this server if you need to treat your AI workforce like a programmable, auditable service layer. This means you care about the lifecycle of the work: knowing when it started (list_agent_tasks), what state it's in (get_task_status), and which specific inputs triggered it (trigger_tool).
Don't use this if your primary need is just generating short, creative text or simple Q&A. If you only need general chat interaction without tracking or orchestration, a standard LLM connection works fine. However, if you are moving beyond single prompts and into multi-step, automated business processes—especially those involving external data sources (RAG) or scheduled background jobs—this server is mandatory. It moves your AI from a chatbot to an actual operational system.
Questions you might have
How do I check the status of a running task using get_task_status? +
You call get_task_status and provide the specific unique Task ID. The result tells you if it's PENDING, IN_PROGRESS, or COMPLETE, giving you real-time visibility on background jobs.
Which tool do I use to run a custom studio? +
You must use trigger_tool. This function requires you to specify the exact Studio name and all necessary parameters defined when the tool was built. It's more precise than just talking to an agent.
What is list_agents for? +
list_agents gives you a comprehensive roster of every autonomous AI persona active in your account. This helps you quickly identify which specialized worker can handle the task at hand.
How do I use the `delete_task` function if I need to remove old or sensitive task logs? +
The delete_task tool permanently removes a specific task record. You must provide the exact Task ID, as this action cannot be undone and is used for cleanup or compliance purposes.
What information does `get_knowledge` retrieve about an agent's knowledge base? +
It fetches detailed metadata for a specified knowledge base. You can see the scope, data sources, and contents of the knowledge item without needing to search through every document manually.
If I need to audit performance, how do I use `list_executions`? +
The list_executions tool generates a comprehensive log of all agent activity. This history includes timestamps, inputs used, and final outcomes, allowing you to track system performance over time.
Before starting an agent, how do I check its configuration using `get_agent_details`? +
This tool provides the agent's full metadata. You confirm the agent's intended role, required permissions, and current operational status without triggering any tasks or incurring costs.
What happens if I use `trigger_tool` but provide incorrect parameters? +
The system immediately returns an error code along with a specific message. This feedback identifies exactly which parameter failed validation, letting you correct the input structure right away.
Can my AI automatically trigger another autonomous agent in Relevance AI? +
Yes! Use the trigger_agent tool. Provide the agent_id and the user message/goal, and your agent will initiate the autonomous workflow in your Relevance account instantly.
How do I find my Region Code and API Key? +
The Region Code is in your dashboard URL (e.g., bcbe5a). For the API Key, log in to Relevance AI, navigate to Settings > API Keys, and generate a new secret key.
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