How do I find my Instance ZUID?

You can list all your accessible instances using the `list_zesty_instances` tool, or find it in your Zesty.io URL (it starts with `8-`).

Can I see all content items for a particular model?

Yes, use the `list_content_items` tool with the unique ZUID of the model (starts with `6-`) to retrieve all associated entries.

Is it possible to update content via the agent?

Absolutely. Use the `update_content_item` tool by providing the Model ZUID, the Item ZUID, and a JSON object containing the fields you wish to modify.

Zesty.io MCP Server for Pydantic AI 8 tools — connect in under 2 minutes

Name: Zesty.io
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 Zesty.io 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 Zesty.io "
            "(8 tools)."
        ),
    )

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

Connect your Zesty.io account to any AI agent to streamline your headless CMS operations. This MCP server enables your agent to interact with instances, content models, and data entries (items) directly from natural language.

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

Instance Oversight — List all Zesty.io instances associated with your account and retrieve their metadata
Schema Management — List content models to understand your data structures and identify Model ZUIDs
Content Operations — List, retrieve, create, and update content items within specific models
Technical Auditing — Access instance settings and technical metadata for any of your properties
Workflow Automation — Delete content items and maintain your CMS hierarchy via natural language commands

The Zesty.io 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 Zesty.io to Pydantic AI via MCP

Follow these steps to integrate the Zesty.io 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 Zesty.io with type-safe schemas

Why Use Pydantic AI with the Zesty.io MCP Server

Pydantic AI provides unique advantages when paired with Zesty.io 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 Zesty.io integration code

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

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

Zesty.io + Pydantic AI Use Cases

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

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

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

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

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

Zesty.io MCP Tools for Pydantic AI (8)

These 8 tools become available when you connect Zesty.io to Pydantic AI via MCP:

create_content_item

Requires a JSON object with field values. Create a new content item

delete_content_item

Delete a content item

get_content_item

Get details for a specific content item

get_instance_settings

Get configuration settings for the instance

list_content_items

List content items for a specific model

list_content_models

Use this to identify Model ZUIDs. List all content models for the current instance

list_zesty_instances

List all Zesty.io instances associated with the account

update_content_item

Update an existing content item

Example Prompts for Zesty.io in Pydantic AI

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

"List all Zesty instances I have access to."

"Show me the content items for the 'Press Releases' model (ZUID: '6-ghi-987')."

"Update the title of content item '7-jkl-654' in model '6-ghi-987' to '2024 Product Roadmap'."

Troubleshooting Zesty.io MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Zesty.io + Pydantic AI FAQ

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