How do I find my List ID?

Use the `list_workspaces` tool to see all your workspaces and the lists they contain along with their unique IDs.

Can I filter entries in a list?

The current `list_entries` tool retrieves all items. For complex filtering, you can provide field-specific values when using the create or update tools.

Is it possible to see the field definitions of a list?

Absolutely. Use the `list_elements` tool with a list ID to retrieve all field names, types, and configurations.

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

Name: Zenkit
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 Zenkit 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 Zenkit "
            "(8 tools)."
        ),
    )

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

Connect your Zenkit account to any AI agent to streamline your productivity and project management. This MCP server enables your agent to interact with workspaces, lists (collections), and data entries directly from natural language.

Pydantic AI validates every Zenkit 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.

What you can do

Workspace Oversight — List all workspaces and retrieve their constituent lists and metadata
List Management — Query detailed configurations and field elements for any Zenkit list
Data Operations — List, retrieve, create, and update entries (items) within your collections
Field Discovery — Inspect list elements to understand the data structure and field types
Content Cleanup — Delete entries and maintain your lists directly via natural language commands

The Zenkit 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 Zenkit to Pydantic AI via MCP

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

Why Use Pydantic AI with the Zenkit MCP Server

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

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

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

Zenkit + Pydantic AI Use Cases

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

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

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

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

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

Zenkit MCP Tools for Pydantic AI (8)

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

create_entry

Requires a JSON object with field values. Create a new entry in a list

delete_entry

Delete an entry from a list

get_list_details

Get details for a specific list

get_workspace_details

Get details for a specific workspace

list_elements

List all elements (fields) defined in a list

list_entries

List all entries (items) in a list

list_workspaces

List all workspaces and their lists

update_entry

Update an existing entry

Example Prompts for Zenkit in Pydantic AI

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

"List all my Zenkit workspaces and their collections."

"Show me all entries in the list with ID '98765'."

"Create a new entry in list '98765' with name 'Finish API documentation'."

Troubleshooting Zenkit MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Zenkit + Pydantic AI FAQ

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