Sentry
Local
by: modelcontextprotocol
|category: utility
|2025.06.06 updated
Retrieving and analyzing issues from Sentry.io
Step 1: 生成 Stdio 配置
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mcp-server-sentry: A Sentry MCP server
Overview
A Model Context Protocol server for retrieving and analyzing issues from Sentry.io. This server provides tools to inspect error reports, stacktraces, and other debugging information from your Sentry account.
Tools
get_sentry_issue- Retrieve and analyze a Sentry issue 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
- Full stacktrace
Prompts
sentry-issue- Retrieve issue details from Sentry
- Input:
issue_id_or_url(string): Sentry issue ID or URL
- Returns: Formatted issue details as conversation context
Installation
Using uv (recommended)
When using uv no specific installation is needed. We will
use uvx to directly run mcp-server-sentry.
Using PIP
Alternatively you can install mcp-server-sentry via pip:
pip install mcp-server-sentry
After installation, you can run it as a script using:
python -m mcp_server_sentry
Configuration
Usage with Claude Desktop
Add this to your claude_desktop_config.json:
Using uvx
"mcpServers": {
"sentry": {
"command": "uvx",
"args": ["mcp-server-sentry", "--auth-token", "YOUR_SENTRY_TOKEN"]
}
}
Using docker
"mcpServers": {
"sentry": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/sentry", "--auth-token", "YOUR_SENTRY_TOKEN"]
}
}
Using pip installation
"mcpServers": {
"sentry": {
"command": "python",
"args": ["-m", "mcp_server_sentry", "--auth-token", "YOUR_SENTRY_TOKEN"]
}
}
Usage with VS Code
For quick installation, use one of the one-click installation buttons below...
For manual installation, add the following JSON block to your User Settings (JSON) file in VS Code. You can do this by pressing Ctrl + Shift + P and typing Preferences: Open Settings (JSON).
Optionally, you can add it to a file called .vscode/mcp.json in your workspace. This will allow you to share the configuration with others.
Note that the
mcpkey is needed when using themcp.jsonfile.
Using uvx
{
"mcp": {
"inputs": [
{
"type": "promptString",
"id": "auth_token",
"description": "Sentry Auth Token",
"password": true
}
],
"servers": {
"sentry": {
"command": "uvx",
"args": ["mcp-server-sentry"],
"env": {
"SENTRY_AUTH_TOKEN": "${input:auth_token}"
}
}
}
}
}
Using docker
{
"mcp": {
"inputs": [
{
"type": "promptString",
"id": "auth_token",
"description": "Sentry Auth Token",
"password": true
}
],
"servers": {
"sentry": {
"command": "docker",
"args": ["run", "-i", "--rm", "mcp/sentry"],
"env": {
"SENTRY_AUTH_TOKEN": "${input:auth_token}"
}
}
}
}
}
Usage with Zed
Add to your Zed settings.json:
Using uvx
"context_servers": [
"mcp-server-sentry": {
"command": {
"path": "uvx",
"args": ["mcp-server-sentry", "--auth-token", "YOUR_SENTRY_TOKEN"]
}
}
],
Using pip installation
"context_servers": {
"mcp-server-sentry": {
"command": "python",
"args": ["-m", "mcp_server_sentry", "--auth-token", "YOUR_SENTRY_TOKEN"]
}
},
Debugging
You can use the MCP inspector to debug the server. For uvx installations:
npx @modelcontextprotocol/inspector uvx mcp-server-sentry --auth-token YOUR_SENTRY_TOKEN
Or if you've installed the package in a specific directory or are developing on it:
cd path/to/servers/src/sentry
npx @modelcontextprotocol/inspector uv run mcp-server-sentry --auth-token YOUR_SENTRY_TOKEN
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utility|2025.06.06 updated
