mcp-sentry-custom: A Sentry MCP Server

Overview: What is mcp-sentry-custom MCP Server?

mcp-sentry-custom is an innovative Model Context Protocol (MCP) server specifically designed to interact with Sentry.io or self-hosted Sentry instances, facilitating the retrieval and analysis of error reports, stack traces, and debugging information. This versatile server acts as a bridge between AI applications—such as Claude Desktop, Continue, Cursor, and others—and the comprehensive error tracking and management capabilities offered by Sentry.

🔧 Core Features & MCP Capabilities

mcp-sentry-custom offers robust features that empower advanced integration into various AI workflows. Key functionalities include:

1. Issue Retrieval Tool:

   • The get_sentry_issue tool enables detailed analysis of a specific Sentry issue through its ID or URL.
   • It provides a comprehensive breakdown of the issue, including:
     • Title
     • Issue ID
     • Status
     • Level (informational, warning, error)
     • First seen timestamp
     • Last seen timestamp
     • Event count
     • Full stack trace

2. Issue List Tool:

   • The get_list_issues tool retrieves and analyzes a list of issues for a specified Sentry project.
   • It outputs a structured list containing:
     • Issue ID
     • Status
     • Level
     • First seen timestamp
     • Last seen timestamp
     • Event count
     • Basic issue information

3. Sentry Issue Prompt Tool:

   • The sentry-issue prompt generates formatted details of Sentry issues, useful for integration into broader AI workflows.

The server ensures seamless interaction with MCP clients through structured data models and protocols, providing a consistent interface that enhances utility across different use cases.

⚙️ MCP Architecture & Protocol Implementation

The architecture of mcp-sentry-custom is designed to integrate effectively with the MCP protocol standard. Here's an overview of its implementation:

• Communication Layer:

  • Utilizes HTTP and web-based APIs provided by Sentry for data interaction.
  • Implements authentication mechanisms to ensure secure access.

• Data Processing & Transformation:

  • Parses Sentry data into structured JSON formats, making it compatible with MCP clients.
  • Applies filters and transformations as needed to enhance usability.

Mermaid Diagram: MCP Protocol Flow

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

Mermaid Diagram: Data Architecture

graph TD
    subgraph DataSources
        S[Sentry API]
        T[Triple store or database for storage and analysis]
    end
    subgraph MCPClient
        A[Claude Desktop]
        B[Continue]
        C[Cursor]
    end
    subgraph Server
        D[mcp-sentry-custom server]
    end
    S -->|Query| D
    D -->|Data| T
    T -->|Feedback| D
    A,B,C -->|Context Requests| D

🚀 Getting Started with Installation

Installing via Smithery

To automatically install mcp-sentry-custom for use in Claude Desktop, follow these steps:

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

Using uv (Recommended)

For direct installation and running with uv, use the following command:

uvx mcp-sentry-custom --auth-token YOUR_SENTRY_TOKEN --project-slug YOUR_PROJECT_SLUG --organization-slug YOUR_ORGANIZATION_SLUG --sentry-url YOUR_SENTRY_URL

Using pip

Alternatively, you can install mcp-sentry-custom via pip:

pip install mcp-sentry-custom

Or, with development setup using uv:

uv pip install -e .

After installation, run the server script as follows:

python -m mcp_sentry

💡 Key Use Cases in AI Workflows

Example 1: Real-Time Debugging

• Context: An AI developer is working on a complex application that constantly generates errors.
• Workflow:

  {
    "mcpServers": {
      "sentry": {
        "command": "npx",
        "args": ["@smithery/cli", "install", "@javaDer/mcp-sentry-custom"],
        "env": {
          "SENTRY_AUTH_TOKEN": "YOUR_SENTRY_TOKEN"
        }
      }
    }
  }
  

• Outcome: The server continuously monitors Sentry issues and alerts the developer of critical errors in real-time, enabling prompt resolution.

Example 2: Scheduled Data Analysis Reports

• Context: A data science team needs regular reports on error trends.
• Workflow:

  {
    "mcpServers": {
      "sentry": {
        "command": "npx",
        "args": ["@smithery/cli", "install", "@javaDer/mcp-sentry-custom"],
        "env": {
          "SENTRY_AUTH_TOKEN": "YOUR_SENTRY_TOKEN"
        }
      }
    }
  }
  

• Outcome: The data science team receives scheduled reports that aggregate error logs and provide actionable insights, facilitating strategic decision-making.

🔌 Integration with MCP Clients

Claude Desktop

Add the following configuration to your claude_desktop_config.json:

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": [
        "-y", "@smithery/cli install @javaDer/mcp-sentry-custom --client claude"
      ],
      "env": {
        "TOKEN": "YOUR_TOKEN"
      }
    }
  }
}

Continue

Add the following configuration to your settings.json in Continue:

{
  "context_servers": {
    "mcp-sentry-custom": {
      "command": "@smithery/cli",
      "args": ["install", "@javaDer/mcp-sentry-custom --client continue"],
      "env": {
        "API_KEY": "YOUR_API_KEY"
      }
    }
  }
}

Cursor

Add the following configuration to your settings.json in Cursor:

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": [
        "-y", "@smithery/cli install @javaDer/mcp-sentry-custom --client cursor"
      ],
      "env": {
        "API_SECRET_KEY": "YOUR_API_SECRET_KEY"
      }
    }
  }
}

📊 Performance & Compatibility Matrix

Client Compatibility

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

🛠️ Advanced Configuration & Security

Environment Variables

• SENTRY_AUTH_TOKEN: Your Sentry API token.
• Sentry_PROJECT_ID: ID of the Sentry project.
• MCP_SERVER_URL: URL for MCP server.

Security Practices

• Use environment variables to store sensitive information like API tokens and keys.
• Implement rate limiting to prevent abuse.
• Conduct regular security audits.

Example Configuration:

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

❓ Frequently Asked Questions (FAQ)

Q1: How does mcp-sentry-custom integrate with different AI clients?

• A: The server supports integration with various MCP clients, including Claude Desktop, Continue, and Cursor. You can configure these through environment variables and settings in your application.

Q2: Can I customize the data processing logic for Sentry issues?

• A: Yes, you can extend or modify the data processing using custom scripts or plugins to fit specific needs.

Q3: How does mcp-sentry-custom handle different versions of Sentry APIs?

• A: The server is designed to handle multiple API versions by maintaining backward compatibility. Regular updates ensure seamless operation with newer Sentry releases.

Q4: What security measures are in place for data transmission and storage?

• A: Data is transmitted over secure connections using HTTPS, and stored securely following best-practice encryption techniques.

Q5: How does mcp-sentry-custom manage data privacy and compliance with regulations like GDPR?

• A: The server adheres to strict data handling guidelines. Personal information is anonymized where applicable, ensuring compliance with international data protection laws.

👨‍💻 Development & Contribution Guidelines

To contribute to the project, follow these steps:

1. Fork the Repository:

   • Visit the GitHub repository and fork it into your account.

2. Set Up the Development Environment:

   • Install necessary dependencies using pip.
   • Set up environment variables for local development.

3. Make Changes:

   • Develop new features or fix bugs within a feature branch.
   • Ensure tests pass by running local unit tests.

4. Submit Pull Requests:

   • Open a pull request to merge your changes into the main repository.

5. Join the Community:

   • Participate in discussions and contribute to documentation improvements.

🌐 MCP Ecosystem & Resources

• MCP Documentation: Model Context Protocol Docs
• GitHub Repository: mcp-sentry-custom GitHub Repo
• Support Channels:
  • Slack Community: Join the Model Context Protocol community for real-time support.
  • Issue Tracker: Open issues or request features on the GitHub issue tracker.

By leveraging mcp-sentry-custom, developers can significantly enhance their AI applications with robust error tracking and management capabilities, ensuring seamless operations and improved user experience.