Can I check my API gateway metrics through the agent?

Yes. The `get_metrics` tool allows your AI agent to retrieve real-time traffic and latency data for any specific project ID, helping you monitor gateway performance directly through chat.

How do I create a new consumer identity for API key authentication?

You can use the `create_consumer` tool. Provide the project ID and a JSON payload with the consumer's details, and Zuplo will provision the new identity for your API gateway instantly.

Is it possible to see my project's configuration files via conversation?

Absolutely. Use the `list_project_files` tool to retrieve the declarative configuration files for your API project, allowing you to audit routing and policy definitions through your agent.

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

Name: Zuplo
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 Zuplo through the 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 Zuplo "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Zuplo?"
    )
    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 Zuplo MCP Server

Connect your Zuplo account to any AI agent and manage your programmable API infrastructure through natural conversation.

Pydantic AI validates every Zuplo tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the 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

Project Management — List all Zuplo projects and API gateways in your account to monitor your integration landscape
Edge Deployments — Trigger new configuration deployments to the global edge network or audit previous deployment history
Consumer Governance — Create, manage, and revoke API key consumers to control who has access to your gateway authentication
Environment Auditing — List available project environments (e.g., working, production) and verify their current operational status
DNS & Domains — Retrieve and manage configured custom DNS domains for each of your API projects directly from your agent
Real-Time Metrics — Monitor gateway traffic, latency, and throughput metrics directly from Zuplo's edge analytics network
Declarative Config — Browse and list the configuration files that define your gateway's routing and policy logic

The Zuplo 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 Zuplo to Pydantic AI via MCP

Follow these steps to integrate the Zuplo 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 Zuplo with type-safe schemas

Why Use Pydantic AI with the Zuplo MCP Server

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

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

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

Zuplo + Pydantic AI Use Cases

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

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

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

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

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

Zuplo MCP Tools for Pydantic AI (10)

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

create_consumer

Create API key consumer

create_deployment

Create a new deployment

delete_consumer

Delete API key consumer

get_metrics

Get edge metrics

list_consumers

List API key consumers

list_custom_domains

List custom domains

list_deployments

List deployments

list_environments

g., working, production) for a Project. List project environments

list_files

List configuration files

list_projects

List Zuplo projects

Example Prompts for Zuplo in Pydantic AI

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

"List all my Zuplo projects."

"Check the latency metrics for 'proj-101'."

"Create a new consumer for 'Acme Partner' in project 'proj-101'."

Troubleshooting Zuplo MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Zuplo + Pydantic AI FAQ

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