Relevance AI MCP Server for Pydantic AIGive Pydantic AI instant access to 11 tools to Delete Task, Get Agent Details, Get Knowledge, and more

Name: Relevance AI
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Relevance AI through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Ask AI about this MCP Server for Pydantic AI

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The Relevance AI MCP Server for Pydantic AI is a standout in the Industry Titans category — giving your AI agent 11 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

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+ other MCP clients

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Relevance AI "
            "(11 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Relevance AI?"
    )
    print(result.data)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

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IAMAccess control

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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 Relevance AI account to any AI agent and take full control of your autonomous AI workforce and tool orchestration through natural conversation. Relevance AI provides a world-class platform for building and scaling multi-agent systems, and this integration allows you to trigger autonomous agents, execute custom studios (tools), and monitor long-running task histories directly from your chat interface.

Pydantic AI validates every Relevance AI tool response against typed schemas, catching data inconsistencies at build time. Connect 11 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

What you can do

Agent & Workforce Orchestration — List all available autonomous agents and trigger them to perform specific goals with dynamic inputs programmatically.
Studio & Tool Intelligence — Access and monitor your custom AI 'Studios' and execute them with complex parameters directly from the AI interface.
Task Lifecycle Management — Retrieve real-time progress for background tasks and monitor final outputs to ensure your autonomous workflows are always synchronized.
Knowledge & RAG Control — List and search through your agent's knowledge base items and datasets via natural language.
Operational Monitoring — Track system activity and manage regional deployments using simple AI commands.

The Relevance AI MCP Server exposes 11 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 11 Relevance AI tools available for Pydantic AI

When Pydantic AI connects to Relevance AI through Vinkius, your AI agent gets direct access to every tool listed below — spanning multi-agent-systems, autonomous-agents, workflow-automation, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

delete

Delete task on Relevance AI

Permanently delete a task record

get

Get agent details on Relevance AI

Get metadata for an agent

get

Get knowledge on Relevance AI

Get details for a knowledge base

get

Get task status on Relevance AI

Check status and results of a task

list

List agent tasks on Relevance AI

List recent agent tasks

list

List agents on Relevance AI

List all AI agents

list

List executions on Relevance AI

List all agent execution history

list

List knowledge items on Relevance AI

List knowledge base items

list

List tools on Relevance AI

List all studios/tools

trigger

Trigger agent on Relevance AI

Start an agent task

trigger

Trigger tool on Relevance AI

Execute a specific tool (Studio)

Connect Relevance AI to Pydantic AI via MCP

Follow these steps to wire Relevance AI into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 11 tools from Relevance AI with type-safe schemas

Why Use Pydantic AI with the Relevance AI MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Relevance AI integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Relevance AI connection logic from agent behavior for testable, maintainable code

Relevance AI + Pydantic AI Use Cases

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

Type-safe data pipelines: query Relevance AI with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Relevance AI tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Relevance AI and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Relevance AI responses and write comprehensive agent tests

Example Prompts for Relevance AI in Pydantic AI

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

"List all my available autonomous agents."

"Show me all AI agents in my workspace with their execution statistics from the last 7 days."

"Trigger the Lead Qualifier agent to analyze and score a batch of 50 new inbound leads."

Troubleshooting Relevance AI MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Relevance AI + Pydantic AI FAQ

Common questions about integrating Relevance AI MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Relevance AI MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.

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