sentry-mcp

# sentry-mcp [](https://codecov.io/gh/getsentry/sentry-mcp) Sentry's MCP service is primarily designed for human-in-the-loop coding agents. Our tool selection and priorities are focused on developer workflows and debugging use cases, rather than providing a general-purpose MCP server for all Sentry functionality. This remote MCP server acts as middleware to the upstream Sentry API, optimized for coding assistants like Cursor, Claude Code, and similar development tools. It's based on [Cloudflare's work towards remote MCPs](https://blog.cloudflare.com/remote-model-context-protocol-servers-mcp/). ## Getting Started You'll find everything you need to know by visiting the deployed service in production: <https://mcp.sentry.dev> If you're looking to contribute, learn how it works, or to run this for self-hosted Sentry, continue below. ### Stdio vs Remote While this repository is focused on acting as an MCP service, we also support a `stdio` transport. This is still a work in progress, but is the easiest way to adapt run the MCP against a self-hosted Sentry install. **Note:** The AI-powered search tools (`search_events` and `search_issues`) require an OpenAI API key. These tools use natural language processing to translate queries into Sentry's query syntax. Without the API key, these specific tools will be unavailable, but all other tools will function normally. To utilize the `stdio` transport, you'll need to create an User Auth Token in Sentry with the necessary scopes. As of writing this is: ``` org:read project:read project:write team:read team:write event:write ``` Launch the transport: ```shell npx @sentry/mcp-server@latest --access-token=sentry-user-token --host=sentry.example.com ``` Note: You can also use environment variables: ```shell SENTRY_ACCESS_TOKEN= SENTRY_HOST= OPENAI_API_KEY= # Required for AI-powered search tools (search_events, search_issues) ``` ### MCP Inspector MCP includes an [Inspector](https://modelcontextprotocol.io/docs/tools/inspector), to easily test the service: ```shell pnpm inspector ``` Enter the MCP server URL (<http://localhost:5173>) and hit connect. This should trigger the authentication flow for you. Note: If you have issues with your OAuth flow when accessing the inspector on `127.0.0.1`, try using `localhost` instead by visiting `http://localhost:6274`. ## Local Development To contribute changes, you'll need to set up your local environment: 1. **Set up environment files:** ```shell make setup-env # Creates both .env files from examples ``` 2. **Create an OAuth App in Sentry** (Settings => API => [Applications](https://sentry.io/settings/account/api/applications/)): - Homepage URL: `http://localhost:5173` - Authorized Redirect URIs: `http://localhost:5173/callback` - Note your Client ID and generate a Client secret 3. **Configure your credentials:** - Edit `.env` in the root directory and add your `OPENAI_API_KEY` - Edit `packages/mcp-cloudflare/.env` and add: - `SENTRY_CLIENT_ID=your_development_sentry_client_id` - `SENTRY_CLIENT_SECRET=your_development_sentry_client_secret` - `COOKIE_SECRET=my-super-secret-cookie` 4. **Start the development server:** ```shell pnpm dev ``` ### Verify Run the server locally to make it available at `http://localhost:5173` ```shell pnpm dev ``` To test the local server, enter `http://localhost:5173/mcp` into Inspector and hit connect. Once you follow the prompts, you'll be able to "List Tools". ### Tests There are two test suites included: basic unit tests, and some evaluations. Unit tests can be run using: ```shell pnpm test ``` Evals will require a `.env` file in the project root with some config: ```shell # .env (in project root) OPENAI_API_KEY= # Also required for AI-powered search tools in production ``` Note: The root `.env` file provides defaults for all packages. Individual packages can have their own `.env` files to override these defaults during development. Once that's done you can run them using: ```shell pnpm eval ``` ## Development Notes ### Automated Code Review This repository uses automated code review tools (like Cursor BugBot) to help identify potential issues in pull requests. These tools provide helpful feedback and suggestions, but **we do not recommend making these checks required** as the accuracy is still evolving and can produce false positives. The automated reviews should be treated as: - ✅ **Helpful suggestions** to consider during code review - ✅ **Starting points** for discussion and improvement - ❌ **Not blocking requirements** for merging PRs - ❌ **Not replacements** for human code review When addressing automated feedback, focus on the underlying concerns rather than strictly following every suggestion.