How do I get a TeamUp API key?

Visit goteamup.com/developers to request developer API access. You'll receive an API key that must be passed in the X-Api-Key header with each request.

TeamUp MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: TeamUp
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

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

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 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 TeamUp "
            "(10 tools)."
        ),
    )

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

asyncio.run(main())

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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 TeamUp MCP Server

Connect your TeamUp studio to any AI agent and manage your fitness business through natural conversation.

Pydantic AI validates every TeamUp tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Events — View scheduled classes and events with coaches, venues, and capacity
Event Types — Browse class formats: CrossFit, Yoga, HIIT, Open Gym
Registrations — Track who booked which class and check-in status
Customers — Search members, view profiles, and track attendance
Memberships — Browse all plans with pricing and billing frequency
Coaches — List trainers with certifications and class assignments
Venues — Manage training locations and room configurations
Payments — Track revenue from memberships, drop-ins, and packages

The TeamUp MCP Server exposes 10 tools through the Vinkius. Connect it to Pydantic AI 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 TeamUp to Pydantic AI via MCP

Follow these steps to integrate the TeamUp MCP Server with Pydantic AI.

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 10 tools from TeamUp with type-safe schemas

Why Use Pydantic AI with the TeamUp MCP Server

Pydantic AI provides unique advantages when paired with TeamUp 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 TeamUp integration code

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

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

TeamUp + Pydantic AI Use Cases

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

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

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

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

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

TeamUp MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect TeamUp to Pydantic AI via MCP:

get_customer

Get member profile

get_event

Get event details

list_coaches

List coaches and trainers

list_event_types

Includes description, default duration, and default coach. List class/event types

list_events

Filter by date range. List scheduled classes/events

list_memberships

List membership plans

list_payments

Includes amount, method, and member. List payments and revenue

list_registrations

Shows check-in status, waitlist position, and booking time. List event registrations

list_venues

Includes address and capacity. List training venues

search_customers

Returns profile, active membership, visit count, and account balance. Search members

Example Prompts for TeamUp in Pydantic AI

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

"What's this week's class schedule?"

"Check the availability of exactly 'Conference Room A' for tomorrow afternoon."

"Schedule a new event 'Q1 Review Review' in the Executive Calendar for next Monday at 10 AM."

Troubleshooting TeamUp MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

TeamUp + Pydantic AI FAQ

Common questions about integrating TeamUp 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 TeamUp MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.