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Sentry Mcp

Sentry Mcp

Local
github.com/Zzzccs123Developer Tools0Updated 84w ago
Repository

About Sentry Mcp

Sentry Mcp is a TypeScript MCP server for querying and analyzing Sentry issues. It provides issue details, including status, event counts, and stack traces, to AI models.

Key features

  1. 01Retrieve issues by ID or URL
  2. 02Return issue status and level
  3. 03Show timestamps and event counts
  4. 04Include complete stack traces
  5. 05Provide a formatted issue prompt

Use cases

  1. 01Reviewing a Sentry issue in an AI conversation
  2. 02Inspecting an issue's stack trace
  3. 03Checking issue activity and status

MCP support

The MCP server exposes a Sentry issue lookup tool and a prompt template that formats issue details as conversation context.

#sentry#mcp

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

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.

Comments

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New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.