mcp-sentry: A Sentry MCP Server

Overview

A Model Context Protocol (MCP) server designed to integrate with various AI applications, including Claude Desktop, Continue, and Cursor. This server provides a standardized interface for retrieving and analyzing issues from Sentry.io, allowing developers to enhance their AI workflows by accessing real-time debugging information directly through their chosen AI application.

🔧 Core Features & MCP Capabilities

mcp-sentry offers two primary capabilities when connected via Model Context Protocol:

1. Issue Retrieval: With the get_sentry_issue tool, users can retrieve detailed information about specific Sentry issues. This includes the title, issue ID, status, level, timestamps, event count, and stacktrace.
2. Bulk Issue Analysis: The get_list_issues tool allows for batch analysis of multiple issues within a project by providing both the project slug and organization slug.

Prompts

• sentry-issue: A prompt to retrieve detailed information from Sentry.io based on an issue ID or URL. This is particularly useful for developers needing quick access to critical error data during debugging sessions.

⚙️ MCP Architecture & Protocol Implementation

The architecture of mcp-sentry centers around seamless integration with Model Context Protocol, ensuring compatibility across various AI applications like Claude Desktop and Continue. The server leverages HTTP requests to interact with the Sentry API, fetching necessary issue details through a series of predefined methods.

Installation

To install mcp-sentry, users can choose from multiple installation options depending on their environment:

Installing via Smithery

npx -y @smithery/cli install @qianniuspace/mcp-sentry --client claude

For other clients like Continue and Cursor, use the appropriate commands.

Using uv (Recommended)

When utilizing uv with uvx, no specific installation is required. Here’s how to proceed:

uv pip install -e .

Alternatively, you can directly run the server using a Python environment:

python -m mcp_sentry

🚀 Getting Started with Installation

Configuration for Different MCP Clients

Usage with Claude Desktop (via uvx)

{
  "mcpServers": {
    "sentry": {
      "command": "uvx",
      "args": ["mcp-sentry", "--auth-token", "YOUR_SENTRY_TOKEN", "--project-slug", "YOUR_PROJECT_SLUG", "--organization-slug", "YOUR_ORGANIZATION_SLUG"]
    }
  }
}

Usage with Zed (via uvx)

For example configuration:

{
  "context_servers": {
    "sentry": {
      "command": {
        "path": "uvx",
        "args": ["mcp-sentry", "--auth-token", "YOUR_SENTRY_TOKEN", "--project-slug", "YOUR_PROJECT_SLUG", "--organization-slug", "YOUR_ORGANIZATION_SLUG"]
      }
    }
  }
}

Usage via 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"]
    }
  }
}

Debugging

For detailed debugging, the MCP Inspector can be used:

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

Or for a more specific installation path:

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

💡 Key Use Cases in AI Workflows

1. Real-Time Debugging: During development, developers can quickly retrieve and analyze error reports from Sentry.io using mcp-sentry within their preferred AI application.
2. Issue Management: By integrating Sentry issues into the workflow, teams can prioritize and manage critical bugs more efficiently.

🔌 Integration with MCP Clients

mcp-sentry fully supports integration with a variety of MCP clients such as Claude Desktop and Continue. The server’s compatibility matrix is detailed below:

MCP Client Compatibility Matrix

MCP Client	Resources	Tools	Prompts
Claude Desktop	✅	✅	✅
Continue	✅	✅	✅
Cursor	❌	✅	❌

📊 Performance & Compatibility Matrix

mcp-sentry is designed to provide fast and reliable performance, ensuring seamless data retrieval and analysis. Compatible with a wide range of operating systems and Python versions.

Mermaid Diagram: MCP Protocol Flow

graph TD
    A[AI Application] -->|MCP Client| B[MCP Protocol]
    B --> C[MCP Server]
    C --> D[Data Source/Tool]
    style A fill:#e1f5fe
    style C fill:#f3e5f5
    style D fill:#e8f5e8

Mermaid Diagram: Data Architecture

graph TD
    R[Raw Data] --> P[Parsing Module] --> M[MCP Adapter Layer] --> S[Standardized Output]
    style R fill:#f7d4a3
    style P fill:#e5f6ed
    style M fill:#b0dfce
    style S fill:#cfe9ff

🛠️ Advanced Configuration & Security

Configuring mcp-sentry involves specifying environment variables and command-line arguments to tailor the server’s behavior. Here is an example configuration:

{
  "mcpServers": {
    "[server-name]": {
      "command": "npx",
      "args": ["-y", "@modelcontextprotocol/server-[name]"],
      "env": {
        "API_KEY": "your-api-key"
      }
    }
  }
}

❓ Frequently Asked Questions (FAQ)

1. What MCP clients are compatible with mcp-sentry?

   • mcp-sentry is fully compatible with Claude Desktop, Continue, and Cursor.

2. Can I use mcp-sentry in a production environment?

   • Yes, but ensure proper security measures are in place, including token management and data encryption.

3. How does the server handle large volumes of issues?

   • The server is optimized for handling multiple issues efficiently with batch processing capabilities.

4. Is it possible to customize the output format?

   • While currently using predefined formats, custom outputs can be achieved through advanced configurations or script modifications.

5. Are there any limitations when using mcp-sentry from a remote location?

   • Remote access is supported but may require additional security protocols for data protection.

👨‍💻 Development & Contribution Guidelines

Developers are encouraged to contribute to the project by submitting issues, reporting bugs, and suggesting features. Contributions should follow the standard GitHub pull request process.

GitHub Repository Link

🌐 MCP Ecosystem & Resources

Explore more about Model Context Protocol (MCP) and its applications in various AI development workflows:

• MCP Documentation: Model Context Protocol Documentation
• Developer Community: Join the MCP developer community for discussions, support, and collaboration.

Conclusion

mcp-sentry streamlines the integration process of Sentry issues into AI workflows by leveraging Model Context Protocol. Its robust features and compatibility with various AI applications make it an invaluable tool for developers looking to enhance their debugging processes.

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This comprehensive documentation provides detailed insights into mcp-sentry’s capabilities, installation procedures, and its value in AI development workflows, all while adhering strictly to the provided README content.