Overview of the kintone MCP Server

The kintone MCP server bridges AI assistants with the popular cloud‑based business platform kintone, enabling natural language interaction with records, forms, and workflows. By exposing a Model Context Protocol (MCP) endpoint, the server turns kintone’s REST API into a set of intuitive AI tools that can be invoked directly from Claude Desktop or any MCP‑compliant client. Developers no longer need to write custom integration code; instead they can ask questions or trigger updates in plain English, and the server translates those intents into authenticated API calls.

At its core, the server authenticates against a kintone tenant using either user credentials or API tokens. It then exposes three primary capabilities: querying records, modifying fields, and listing available applications. The MCP interface presents these as conversational tools—“Get Record”, “Update Field”, and “List Apps”—which the assistant can call with structured arguments. Because the server enforces access control via and , developers can fine‑tune which app data the AI may read or write, ensuring compliance with internal security policies.

Key features include:

• Secure authentication: Supports both username/password and token‑based access, allowing flexibility for different deployment environments.
• Granular app permissions: Allows or denies specific kintone apps, giving teams tight control over data exposure.
• Rich toolset: Exposes record retrieval, updates, and app discovery as AI‑friendly tools that can be chained in complex workflows.
• Zero code integration: Once configured, AI assistants can issue kintone commands without any additional programming.

Typical use cases are abundant in enterprise settings. A project manager can ask the assistant, “Show me all tasks overdue for Project X,” and receive a real‑time list pulled directly from kintone. A sales rep can say, “Mark lead #1234 as contacted,” and the server will update the corresponding record. Customer support teams can automate ticket status updates, while HR departments can query employee onboarding progress—all through conversational prompts.

Integration with AI workflows is seamless. The MCP server registers itself in the client’s tool registry, so each prompt can automatically surface relevant kintone actions. Developers can compose multi‑step dialogues: the assistant first retrieves a record, then asks for confirmation before performing an update. Because the server handles all HTTP communication and error translation, developers can focus on designing conversational flows rather than boilerplate API handling.

In summary, the kintone MCP server empowers developers to unlock kintone data for AI‑driven automation, reporting, and decision support. By providing a secure, permissioned bridge between natural language interfaces and enterprise data, it accelerates the adoption of AI assistants across business processes while maintaining strict control over sensitive information.