How do I obtain my Ezus API Key?

You can find your API Key in the Ezus settings under the 'Integrations' or 'API' section. Note that a Premium plan may be required for private API access.

Can I update existing projects using this integration?

Yes! Use the `upsert_project` tool. If you provide an existing Project ID, it will update the project; otherwise, it will create a new one.

Is it possible to list invoices to track payments?

Absolutely. The `list_invoices` and `get_invoice` tools allow you to retrieve financial records and check their current status.

Ezus MCP Server for Pydantic AI 12 tools — connect in under 2 minutes

Name: Ezus
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 12 Tools SDK

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

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

asyncio.run(main())

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* 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 Ezus MCP Server

Connect your Ezus travel management account to any AI agent and take full control of your agency's workflows through natural conversation.

Pydantic AI validates every Ezus tool response against typed schemas, catching data inconsistencies at build time. Connect 12 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, fetch, and upsert travel projects directly from the Ezus cloud
Client & Supplier CRM — Query client details and manage your network of suppliers with ease
Product Catalog — Access and inspect your travel products and packages stored in the Ezus catalog
Financial Overview — List and inspect invoices to keep track of your agency's billing and financial status
User Profiling — Retrieve the underlying credentials and profile information of your agent's API user

The Ezus MCP Server exposes 12 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 Ezus to Pydantic AI via MCP

Follow these steps to integrate the Ezus 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 12 tools from Ezus with type-safe schemas

Why Use Pydantic AI with the Ezus MCP Server

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

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

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

Ezus + Pydantic AI Use Cases

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

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

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

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

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

Ezus MCP Tools for Pydantic AI (12)

These 12 tools become available when you connect Ezus to Pydantic AI via MCP:

get_client

Get a specific Ezus client by ID

get_invoice

Get a specific Ezus invoice by ID

get_me

Get current Ezus user profile

get_product

Get a specific Ezus product by ID

get_project

Get a specific Ezus project by ID

get_supplier

Get a specific Ezus supplier by ID

list_clients

List all Ezus clients

list_invoices

List all Ezus invoices

list_products

List all Ezus products

list_projects

List all Ezus projects

list_suppliers

List all Ezus suppliers

upsert_project

Create or update an Ezus project

Example Prompts for Ezus in Pydantic AI

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

"List my recent travel projects on Ezus."

"Show me the details for client ID 12345."

"Get all products available in the catalog."

Troubleshooting Ezus MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Ezus + Pydantic AI FAQ

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