What does Zendesk MCP Server do?
It integrates Zendesk with MCP-compatible AI tools, allowing AI agents to read, comment on, and analyze Zendesk tickets, and access Help Center articles as a knowledge base.
@reminia
基本情報
A Model Context Protocol server for Zendesk
以下の設定を使って、このサーバーを MCP 対応クライアントに追加してください。
{
"mcpServers": {
"zendesk": {
"command": "uv",
"args": [
"--directory",
"/path/to/zendesk-mcp-server",
"run",
"zendesk"
]
}
}
}ツール一覧はまだありません
この掲載には表示できるツール一覧がまだありません。サーバーにツールがないという意味ではありません。対応機能はプロジェクトのドキュメントをご確認ください。
ツールは README から自動的に抽出されます。メンテナーは ## Tools という見出しの下に記載することで、このタブに反映できます。
A Model Context Protocol server that provides a comprehensive integration with Zendesk. It offers tools for retrieving and managing Zendesk tickets and comments, plus specialized prompts for ticket analysis and response drafting. Designed for MCP-compatible clients like Claude Desktop.
Create a Python environment with uv venv && uv pip install -e . or uv build. Set up Zendesk credentials in a .env file (see .env.example). Then configure the server in your MCP client (e.g., Claude Desktop) by adding the zendesk server entry with the uv command pointing to the project directory.
It integrates Zendesk with MCP-compatible AI tools, allowing AI agents to read, comment on, and analyze Zendesk tickets, and access Help Center articles as a knowledge base.
You need Python and `uv` installed. Run `uv venv && uv pip install -e .`, then create a `.env` file with your Zendesk credentials. Finally, configure the server in your MCP client (e.g., Claude Desktop) using the provided JSON block.
Python (with `uv`), the packages listed in the project, and network access to your Zendesk instance. Credentials are stored in a `.env` file.
Data is fetched live from your Zendesk instance via the Zendesk API. No data is stored locally by the server beyond what is needed for the current session.
Yes, you provide your Zendesk credentials (subdomain, email, API token) in the `.env` file. The server uses these to authenticate with the Zendesk API.

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