How do I find the App ID for a specific application?

Use the `list_apps` tool to see a comprehensive list of all applications in your environment along with their unique App IDs.

Can I filter records using custom criteria?

Yes, the `list_records` tool accepts an optional Kintone query string, allowing you to filter data based on field values, status, and more.

Is it possible to update an existing record via the agent?

Absolutely. Use the `update_record` tool with the App ID and Record ID, providing a JSON object with the fields you wish to modify.

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

Name: Kintone
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 Kintone 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 Kintone "
            "(10 tools)."
        ),
    )

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

Connect your Kintone platform to any AI agent to automate your business operations. This MCP server enables your agent to interact with custom apps, manage data records, and query organizational metadata directly.

Pydantic AI validates every Kintone tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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

Record Management — List, retrieve, add, and update records in any of your Kintone apps
App Discovery — List all available applications and retrieve detailed configurations and field mappings
Data Querying — Use Kintone's powerful query language to filter records based on complex criteria
Form Inspection — Access form field settings and layouts to understand data structures
Space Visibility — List members and participants within your Kintone collaboration spaces

The Kintone 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 Kintone to Pydantic AI via MCP

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

Why Use Pydantic AI with the Kintone MCP Server

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

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

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

Kintone + Pydantic AI Use Cases

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

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

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

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

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

Kintone MCP Tools for Pydantic AI (10)

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

add_record

Requires a JSON object mapping field codes to values. Add a new record to an app

delete_records

Requires an array of record IDs. Delete records from an app

get_app_details

Get details for a specific app

get_app_layout

Get the field layout of an app

get_record

Get a specific record from an app

list_apps

Use this to identify App IDs for record operations. List all Kintone apps

list_form_fields

List form fields for an app

list_records

You can optionally provide a query string for filtering. List records from an app

list_space_members

List members of a Kintone space

update_record

Update an existing record

Example Prompts for Kintone in Pydantic AI

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

"List all my Kintone apps."

"Show records from app ID 10 where status is 'Pending'."

"Add a new record to app 12 with name 'Jane Doe' and role 'Designer'."

Troubleshooting Kintone MCP Server with Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Kintone + Pydantic AI FAQ

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