---

name: sentry-mcp-connector description: "MCP connector for Sentry error tracking – enables AI-powered error analysis, issue management, and performance monitoring through Claude" license: BSL tags: ["dev","monitoring","mcp","sentry","error-tracking"] difficulty: intermediate time_to_master: "4-8 weeks" version: "1.0.0"

Sentry MCP Connector

Overview

This skill enables Claude to interact with Sentry through the Model Context Protocol (MCP). It provides a bridge between Claude's AI capabilities and Sentry's REST API, allowing natural language control of Sentry operations, intelligent automation, and AI-powered assistance for Sentry workflows.

When to Use This Skill

• Error event analysis and root cause identification
• Issue triage and assignment via AI
• Alert rule creation and management
• Release tracking and deployment monitoring
• Performance transaction analysis

Architecture

┌─────────────┐     ┌─────────────────┐     ┌──────────────────┐
│   Claude     │────▶│   MCP Server    │────▶│   Sentry           │
│   (Client)   │◀────│   (TypeScript)  │◀────│   (REST API      )│
└─────────────┘     └─────────────────┘     └──────────────────┘

Core Concepts

MCP Server Setup

The connector implements an MCP server that exposes Sentry operations as tools Claude can invoke. The server translates natural language intentions into REST API calls.

Key Endpoints/Interfaces

/api/0/projects/:org/:project/issues/, /api/0/issues/:id/events/, /api/0/projects/:org/:project/releases/, /api/0/organizations/:org/alerts/rules/

Implementation

// Sentry MCP Server Implementation
import { McpServer } from "@modelcontextprotocol/sdk/server/mcp.js";
import { StdioServerTransport } from "@modelcontextprotocol/sdk/server/stdio.js";
import { z } from "zod";

const server = new McpServer({
  name: "sentry-mcp-connector",
  version: "1.0.0",
});

// Tool: List/Query Resources
server.tool(
  "list_resources",
  "List and query Sentry resources with optional filters",
  {
    query: z.string().optional().describe("Search query or filter"),
    limit: z.number().optional().describe("Max results to return"),
  },
  async ({ query, limit }) => {
    // Call Sentry REST API
    const response = await fetch(`${BASE_URL}/api/0/projects/:org/:project/issues/`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Tool: Create Resource
server.tool(
  "create_resource",
  "Create a new resource in Sentry",
  {
    name: z.string().describe("Resource name"),
    config: z.object({}).passthrough().optional().describe("Resource configuration"),
  },
  async ({ name, config }) => {
    const response = await fetch(`${BASE_URL}/api/0/projects/:org/:project/issues/`, {
      method: "POST",
      headers: {
        "Authorization": `Bearer ${API_KEY}`,
        "Content-Type": "application/json",
      },
      body: JSON.stringify({ name, ...config }),
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: `Created: ${JSON.stringify(data)}` }],
    };
  }
);

// Tool: Analyze/Report
server.tool(
  "analyze",
  "AI-powered analysis of Sentry data",
  {
    type: z.string().describe("Analysis type"),
    timeframe: z.string().optional().describe("Time range for analysis"),
  },
  async ({ type, timeframe }) => {
    // Fetch data and provide AI analysis
    const response = await fetch(`${BASE_URL}/api/0/projects/:org/:project/issues/`, {
      headers: { "Authorization": `Bearer ${API_KEY}` },
    });
    const data = await response.json();
    return {
      content: [{ type: "text", text: JSON.stringify(data, null, 2) }],
    };
  }
);

// Start server
const transport = new StdioServerTransport();
await server.connect(transport);

Claude Desktop Configuration

{
  "mcpServers": {
    "sentry-mcp-connector": {
      "command": "node",
      "args": ["path/to/sentry-mcp-connector/index.js"],
      "env": {
        "SENTRY_API_KEY": "your-api-key",
        "SENTRY_BASE_URL": "https://your-instance-url"
      }
    }
  }
}

Best Practices

1. Authentication: Store API keys securely using environment variables; never hardcode credentials
2. Rate Limiting: Implement request throttling to respect Sentry API rate limits
3. Error Handling: Provide clear, actionable error messages for common failure scenarios
4. Pagination: Handle paginated responses for large datasets efficiently
5. Caching: Cache frequently accessed read-only data to reduce API calls
6. Security: Validate all inputs before passing to the Sentry API; sanitize outputs
7. Logging: Log all API interactions for debugging and audit purposes

Example Prompts

"Analyze the top 5 unresolved errors from the last deployment and suggest code fixes"

Security Considerations

• All API credentials must be stored as environment variables
• Implement input validation and sanitization for all tool parameters
• Use HTTPS for all API communications
• Follow the principle of least privilege for API token permissions
• Audit log all write operations for compliance tracking

Resources

• Sentry Official Documentation
• MCP SDK Documentation: https://modelcontextprotocol.io
• MCP Server Examples: https://github.com/modelcontextprotocol/servers
• SkillGalaxy Repository: https://github.com/Sandeeprdy1729/skill_galaxy

Changelog

Version	Date	Changes
1.0.0	2026-04-01	Initial MCP connector skill

---

Part of SkillGalaxy - 10,000+ comprehensive skills for AI-assisted development.