mcp-sentry
A Model Context Protocol server that enables retrieving and analyzing Sentry issues, including stacktraces and debugging information.
README
mcp-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
get_list_issues- Retrieve and analyze Sentry issues by project slug
- Input:
project_slug(string): Sentry project slug to analyzeorganization_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
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:
<details> <summary>Using uvx</summary>
"mcpServers": {
"sentry": {
"command": "uvx",
"args": ["mcp-sentry", "--auth-token", "YOUR_SENTRY_TOKEN","--project-slug" ,"YOUR_PROJECT_SLUG", "--organization-slug","YOUR_ORGANIZATION_SLUG"]
}
}
</details>
<details> <summary>Using docker</summary>
"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"]
}
}
</details>
<details>
<summary>Using pip installation</summary>
"mcpServers": {
"sentry": {
"command": "python",
"args": ["-m", "mcp_sentry", "--auth-token", "YOUR_SENTRY_TOKEN","--project-slug" ,"YOUR_PROJECT_SLUG", "--organization-slug","YOUR_ORGANIZATION_SLUG"]
}
}
</details>
Usage with Zed
Add to your Zed settings.json:
<details> <summary>Using uvx</summary>
For Example Curson
"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"]
}
}
],
</details>
<details> <summary>Using pip installation</summary>
"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"]
}
},
</details>
<details> <summary>Using pip installation with custom path</summary>
"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"
}
}
},
</details>
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

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.
Recommended Servers
playwright-mcp
A Model Context Protocol server that enables LLMs to interact with web pages through structured accessibility snapshots without requiring vision models or screenshots.
Magic Component Platform (MCP)
An AI-powered tool that generates modern UI components from natural language descriptions, integrating with popular IDEs to streamline UI development workflow.
Audiense Insights MCP Server
Enables interaction with Audiense Insights accounts via the Model Context Protocol, facilitating the extraction and analysis of marketing insights and audience data including demographics, behavior, and influencer engagement.
VeyraX MCP
Single MCP tool to connect all your favorite tools: Gmail, Calendar and 40 more.
graphlit-mcp-server
The Model Context Protocol (MCP) Server enables integration between MCP clients and the Graphlit service. Ingest anything from Slack to Gmail to podcast feeds, in addition to web crawling, into a Graphlit project - and then retrieve relevant contents from the MCP client.
Kagi MCP Server
An MCP server that integrates Kagi search capabilities with Claude AI, enabling Claude to perform real-time web searches when answering questions that require up-to-date information.
E2B
Using MCP to run code via e2b.
Neon Database
MCP server for interacting with Neon Management API and databases
Qdrant Server
This repository is an example of how to create a MCP server for Qdrant, a vector search engine.
Exa Search
A Model Context Protocol (MCP) server lets AI assistants like Claude use the Exa AI Search API for web searches. This setup allows AI models to get real-time web information in a safe and controlled way.