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 CrewAI 10 tools — connect in under 2 minutes

Name: Kintone
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 10 Tools Framework

Connect your CrewAI agents to Kintone through Vinkius, pass the Edge URL in the `mcps` parameter and every Kintone tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

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Vinkius supports streamable HTTP and SSE.

python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Kintone Specialist",
    goal="Help users interact with Kintone effectively",
    backstory=(
        "You are an expert at leveraging Kintone tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token. get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Kintone "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 10 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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.

When paired with CrewAI, Kintone becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Kintone tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Kintone MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py. CrewAI auto-discovers 10 tools from Kintone

Why Use CrewAI with the Kintone MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Kintone through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Kintone + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Kintone for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Kintone, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Kintone tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Kintone against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Kintone MCP Tools for CrewAI (10)

These 10 tools become available when you connect Kintone to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

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

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Kintone + CrewAI FAQ

Common questions about integrating Kintone MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.