# MCP Sentry Analyzer

> Use this tool when you need to automatically detect and diagnose frontend JavaScript errors, and receive intelligent repair suggestions through AI-powered analysis. The MCP Sentry Analyzer integrates with Sentry error monitoring to capture errors and provides outputs in the form of repair suggestions through multiple AI models. It is ideal for developers looking to streamline error resolution and improve application reliability.

Canonical page: https://skillsregistry.net/skills/ranruan-mcp  
JSON: https://api.skillsregistry.net/v1/skills/ranruan-mcp

## Description

Integrates Sentry error monitoring with AI-powered analysis to automatically capture frontend JavaScript errors and provide intelligent repair suggestions through multiple AI models including OpenAI, Claude, and Gemini.

## Trust

- **Trust score (0–1):** 0.60
- **Verification tier:** unverified

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** mcp-remote
- **Runtime environment:** api
- **Category:** ai-ml
- **Updated:** 2026-04-22

## Source

- **Source listing:** [Glama](https://glama.ai/mcp/servers/ucciajoxq9)
- **Repository:** <https://github.com/RanRuan/mcp>

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "ranruan-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/ranruan-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/ranruan-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt