Sentry Mcp
About
Sentry Mcp is a Model Context Protocol (MCP) server implemented in TypeScript that connects AI models to the Sentry error tracking service. It allows AI models to query and analyze error reports and events on Sentry.
Details
- Author
- Zzzccs123
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- get_sentry_issue tool retrieves and analyzes Sentry issues by ID or URL.
- sentry-issue prompt template formats issue details as conversation context.
- Returns issue title, ID, status, level, first/last seen timestamps, event count, and complete stack trace.
- Configuration via environment variables with fallback to runtime values.
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Sentry McpCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Install dependencies with npm install, build the project with npm run build, and run the server via standard I/O using node dist/index.js. Set the required environment variable SENTRY_AUTH_TOKEN (and optionally SENTRY_ORGANIZATION_SLUG, SENTRY_PROJECT_SLUG, SENTRY_BASE_URL) in a .env file or at runtime. Debug with the MCP Inspector using npx @modelcontextprotocol/inspector node dist/index.js.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"sentry mcp": {
"sentry-mcp": {
"command": "node",
"args": [
"mcp-server/sentry/dist/index.js"
]
}
}
}
}McpServers
{
"sentry-mcp": {
"command": "node",
"args": [
"mcp-server/sentry/dist/index.js"
]
}
}MCP Server Sentry - TypeScript Implementation
This is a Model Context Protocol (MCP) server implemented in TypeScript for connecting to the Sentry error tracking service. This server allows AI models to query and analyze error reports and events on Sentry.
Features
1. get_sentry_issue Tool
Retrieves and analyzes Sentry issues by ID or URL
Input:
issue_id_or_url (string): Sentry issue ID or URL to analyze
Returns: Issue details including:
Title
Issue ID
Status
Level
First seen timestamp
Last seen timestamp
Event count
Complete stack trace
2. sentry-issue Prompt Template
Retrieves issue details from Sentry
Input:
issue_id_or_url (string): Sentry issue ID or URL
Returns: Formatted issue details as conversation context
Installation
# Install dependencies
npm install
Build the project
npm run build
Configuration
The server is configured using environment variables. Create a .env file in the project root directory:
# Required: Sentry authentication token
SENTRY_AUTH_TOKEN=your_sentry_auth_token
Optional: Sentry organization name
SENTRY_ORGANIZATION_SLUG=your_organization_slug
Optional: Sentry project name
SENTRY_PROJECT_SLUG=your_project_slug
Optional: Sentry base url
SENTRY_BASE_URL=https://sentry.com/api/0
Alternatively, you can set these environment variables at runtime.
Running
Run the server via standard IO:
node dist/index.js
Debug with MCP Inspector:
npx @modelcontextprotocol/inspector node dist/index.js
Environment Variables Description
- SENTRY_AUTH_TOKEN (required): Your Sentry API access token
- SENTRY_PROJECT_SLUG (optional): The slug of your Sentry project
- SENTRY_ORGANIZATION_SLUG (optional): The slug of your Sentry organization
The latter two variables can be omitted if project and organization information are provided in the URL.
License
This project is licensed under the MIT License.
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