kintone MCP Server

Overview

The Kintone MCP Server provides a turnkey bridge between AI assistants—such as Claude—and the popular business‑process platform Kintone. By exposing a Model Context Protocol (MCP) endpoint, the server translates standard MCP calls into authenticated REST requests against a Kintone tenant. This eliminates the need for developers to write custom OAuth flows or low‑level HTTP clients, allowing AI workflows to read and manipulate business data with the same ease as they would interact with any other external service.

At its core, the server implements a set of MCP tools that map directly onto Kintone’s API surface. These include operations for retrieving, creating, updating, and deleting records; querying with Kintone’s query language; uploading files to attachments; and downloading those files back into the AI context. Each tool is accompanied by a concise description, sample arguments, and a clear response schema, so the AI can generate or consume data without ambiguity. The server also supports configuration of proxy settings and custom headers, giving enterprises full control over network routing and security compliance.

For developers building AI‑powered applications, this server unlocks several real‑world scenarios. A sales chatbot can pull the latest lead information from a Kintone CRM, suggest follow‑up actions, and automatically create activity logs. A project management assistant can read task lists, update status fields, or attach meeting notes directly to the relevant Kintone records. Even simple automation scripts—such as nightly data exports or compliance checks—can be written in natural language and executed by the AI, relying on the MCP server to handle all API intricacies.

Integration into existing AI workflows is straightforward. Once installed (via DXT, Docker, or npm), the MCP server exposes a single endpoint that AI clients can register with. The client then declares the Kintone tools it wishes to use, and the AI can invoke them in the same way it calls other built‑in services. Because the server respects Kintone’s rate limits and pagination semantics, developers can build complex data pipelines without worrying about throttling or partial responses.

What sets the Kintone MCP Server apart is its focus on simplicity and security. It bundles all necessary authentication details into a single configuration file, supports environment‑variable overrides for CI/CD pipelines, and includes comprehensive documentation on edge cases such as file size limits or record locking. By abstracting the Kintone API behind MCP, it gives AI assistants a reliable, well‑documented interface to enterprise data while preserving the platform’s native capabilities and governance controls.