Does PushPress have a TypeScript SDK?

Yes! PushPress provides an early-alpha TypeScript SDK on GitHub, and their v3 API is the most modern in the gym management space.

Does PushPress integrate with external waiver systems?

PushPress has a robust internal waiver and document signing system, but its API can also sync status with external CRM and waiver platforms.

Can I use the API to book an appointment for a client?

Yes, the v3 API allows authorized agents to schedule personal training sessions and appointments dynamically based on coach availability.

PushPress MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: PushPress
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 8 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect PushPress 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 PushPress "
            "(8 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

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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 PushPress MCP Server

Connect your PushPress gym to any AI agent.

Pydantic AI validates every PushPress tool response against typed schemas, catching data inconsistencies at build time. Connect 8 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.

Members — Search, profiles, billing
Check-ins — Daily visits with method tracking
Classes — Group classes with coach and capacity
Plans — Memberships, punch cards, trials
Appointments — PT and private coaching
Messages — Emails, push, SMS
Webhooks — Real-time event notifications

Built for CrossFit boxes, independent gyms, and S&C facilities.

The PushPress MCP Server exposes 8 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 PushPress to Pydantic AI via MCP

Follow these steps to integrate the PushPress 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 8 tools from PushPress with type-safe schemas

Why Use Pydantic AI with the PushPress MCP Server

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

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

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

PushPress + Pydantic AI Use Cases

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

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

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

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

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

PushPress MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect PushPress to Pydantic AI via MCP:

get_customer

Get member profile

list_appointments

List PT appointments

list_checkins

List gym check-ins

list_classes

List scheduled classes

list_messages

List sent messages

list_plans

Includes pricing and billing cycle. List membership plans

list_webhooks

List active webhooks

search_customers

Returns profile, active plan, check-in count, and billing status. Search gym members

Example Prompts for PushPress in Pydantic AI

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

"How many people checked in today?"

"Who is enrolled in the 6 AM CrossFit class today?"

"Which members have a billing issue right now?"

Troubleshooting PushPress MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

PushPress + Pydantic AI FAQ

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