mcpField
M
mcp-sentry: A Sentry MCP server

mcp-sentry: A Sentry MCP server

Local
mcpdirs.comDeveloper Tools22Updated 67w ago

About mcp-sentry: A Sentry MCP server

mcp-sentry is an MCP server for retrieving and analyzing issues from Sentry.io. It is aimed at developers who need to inspect error reports and stacktraces from their Sentry account.

Key features

  1. 01Fetch issue details by ID or URL
  2. 02Include full stacktrace in issue output
  3. 03List issues by organization and project slug
  4. 04Provide sentry-issue prompt for issue context

Use cases

  1. 01Inspecting a specific Sentry error from an AI assistant
  2. 02Reviewing the current issue list for a project
  3. 03Loading issue details as conversation context while debugging

MCP support

This project is an MCP server that exposes the get_sentry_issue and get_list_issues tools and the sentry-issue prompt to MCP clients.

Configuration

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

mcp.json · Universal config
{
"mcpServers": {
"sentry": {
"command": "npx",
"args": [
"-y",
"@smithery/cli"
]
}
}
}
Run commandnpx -y @smithery/cli

Local servers need a runtime on your machine (e.g. Node.js 18+). Config file locations differ by client; see your client’s docs.

Packages
npm@smithery/cli

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-sentry: A Sentry MCP server

smithery badge

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
  1. 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
  2. get_list_issues
    • Retrieve and analyze Sentry issues by project slug
    • Input:
      • project_slug (string): Sentry project slug to analyze
      • organization_slug (string): Sentry organization slug to analyze
    • Returns: List of issues with details including:
      • Title
      • Issue ID
      • Status
      • Level
      • First seen timestamp
      • Last seen timestamp
      • Event count
      • Basic issue information
Prompts
  1. 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

Installing via Smithery

To install mcp-sentry for Claude Desktop automatically via Smithery:

npx -y @smithery/cli install @qianniuspace/mcp-sentry --client claude
Using uv (recommended)

When using uv no specific installation is needed. We will use uvx to directly run mcp-sentry.

Using PIP

Alternatively you can install mcp-sentry via pip:

pip install mcp-sentry

or use uv

uv pip install -e .

After installation, you can run it as a script using:

python -m mcp_sentry

Configuration

Usage with Claude Desktop

Add this to your claude_desktop_config.json:

Using uvx
"mcpServers": {
  "sentry": {
    "command": "uvx",
    "args": ["mcp-sentry", "--auth-token", "YOUR_SENTRY_TOKEN","--project-slug" ,"YOUR_PROJECT_SLUG", "--organization-slug","YOUR_ORGANIZATION_SLUG"]
  }
}
Using docker
"mcpServers": {
  "sentry": {
    "command": "docker",
    "args": ["run", "-i", "--rm", "mcp/sentry", "--auth-token", "YOUR_SENTRY_TOKEN","--project-slug" ,"YOUR_PROJECT_SLUG", "--organization-slug","YOUR_ORGANIZATION_SLUG"]
  }
}
Using pip installation
"mcpServers": {
  "sentry": {
    "command": "python",
    "args": ["-m", "mcp_sentry", "--auth-token", "YOUR_SENTRY_TOKEN","--project-slug" ,"YOUR_PROJECT_SLUG", "--organization-slug","YOUR_ORGANIZATION_SLUG"]
  }
}
Usage with Zed

Add to your Zed settings.json:

Using uvx

For Example Curson mcp.json

"context_servers": [
  "mcp-sentry": {
    "command": {
      "path": "uvx",
      "args": ["mcp-sentry", "--auth-token", "YOUR_SENTRY_TOKEN","--project-slug" ,"YOUR_PROJECT_SLUG", "--organization-slug","YOUR_ORGANIZATION_SLUG"]
    }
  }
],
Using pip installation
"context_servers": {
  "mcp-sentry": {
    "command": "python",
    "args": ["-m", "mcp_sentry", "--auth-token", "YOUR_SENTRY_TOKEN","--project-slug" ,"YOUR_PROJECT_SLUG", "--organization-slug","YOUR_ORGANIZATION_SLUG"]
  }
},
Using pip installation with custom path
"context_servers": {
  "sentry": {
      "command": "python",
      "args": [
        "-m",
        "mcp_sentry",
        "--auth-token",
        "YOUR_SENTRY_TOKEN",
        "--project-slug",
        "YOUR_PROJECT_SLUG",
        "--organization-slug",
        "YOUR_ORGANIZATION_SLUG"
      ],
      "env": {
        "PYTHONPATH": "path/to/mcp-sentry/src"
      }
    }
},

Debugging

You can use the MCP inspector to debug the server. For uvx installations:

npx @modelcontextprotocol/inspector uvx mcp-sentry --auth-token YOUR_SENTRY_TOKEN --project-slug YOUR_PROJECT_SLUG --organization-slug YOUR_ORGANIZATION_SLUG

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-sentry --auth-token YOUR_SENTRY_TOKEN --project-slug YOUR_PROJECT_SLUG --organization-slug YOUR_ORGANIZATION_SLUG  

or in term

npx @modelcontextprotocol/inspector uv --directory /Volumes/ExtremeSSD/MCP/mcp-sentry/src run mcp_sentry --auth-token YOUR_SENTRY_TOKEN
--project-slug YOUR_PROJECT_SLUG --organization-slug YOUR_ORGANIZATION_SLUG

Inspector-tools

Fork From

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

Comments

Markdown supported

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.