developer-tools

Sentry

getsentry

by getsentry

Easily integrate and debug Sentry APIs with sentry-mcp, a flexible MCP middleware for cloud and self-hosted setups.

Streamline Sentry API integration with this remote MCP server middleware prototype. sentry-mcp acts as a bridge between clients and Sentry, supporting flexible transport methods and offering tools like the MCP Inspector for easy service testing. Inspired by Cloudflare’s remote MCP initiative, it helps developers adapt and debug workflows, making Sentry interaction smoother for both cloud and self-hosted environments.

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Direct Sentry.io integrationFull stacktrace access

best for

  • / Developers debugging production issues
  • / Support teams investigating error reports
  • / DevOps analyzing application stability
  • / Teams streamlining issue triage workflows

capabilities

  • / Retrieve Sentry issues by ID or URL
  • / List issues from specific projects
  • / View detailed stacktraces and error data
  • / Access issue metadata like timestamps and event counts
  • / Analyze error reports across organizations

what it does

Connects to Sentry.io to retrieve error reports, stacktraces, and debugging information from your projects. Helps developers analyze and track issues directly through the MCP interface.

about

Sentry is an official MCP server published by getsentry that provides AI assistants with tools and capabilities via the Model Context Protocol. Easily integrate and debug Sentry APIs with sentry-mcp, a flexible MCP middleware for cloud and self-hosted setups. It is categorized under developer tools.

how to install

You can install Sentry in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.

license

NOASSERTION

Sentry is released under the NOASSERTION license.

readme

sentry-mcp

Sentry's MCP service is primarily designed for human-in-the-loop coding agents. Our tool selection and priorities are focused on developer workflows and debugging use cases, rather than providing a general-purpose MCP server for all Sentry functionality.

This remote MCP server acts as middleware to the upstream Sentry API, optimized for coding assistants like Cursor, Claude Code, and similar development tools. It's based on Cloudflare's work towards remote MCPs.

Getting Started

You'll find everything you need to know by visiting the deployed service in production:

https://mcp.sentry.dev

If you're looking to contribute, learn how it works, or to run this for self-hosted Sentry, continue below.

Claude Code Plugin

Install as a Claude Code plugin for automatic subagent delegation:

claude plugin marketplace add getsentry/sentry-mcp
claude plugin install sentry-mcp@sentry-mcp

This provides a sentry-mcp subagent that Claude automatically delegates to when you ask about Sentry errors, issues, traces, or performance.

For experimental features:

claude plugin install sentry-mcp@sentry-mcp-experimental

Stdio vs Remote

While this repository is focused on acting as an MCP service, we also support a stdio transport. This is still a work in progress, but is the easiest way to adapt run the MCP against a self-hosted Sentry install.

Note: The AI-powered search tools (search_events, search_issues, etc.) require an LLM provider (OpenAI or Anthropic). These tools use natural language processing to translate queries into Sentry's query syntax. Without a configured provider, these specific tools will be unavailable, but all other tools will function normally.

To utilize the stdio transport, you'll need to create an User Auth Token in Sentry with the necessary scopes. As of writing this is:

org:read
project:read
project:write
team:read
team:write
event:write

Launch the transport:

npx @sentry/mcp-server@latest --access-token=sentry-user-token

Need to connect to a self-hosted deployment? Add <code>--host</code> (hostname only, e.g. <code>--host=sentry.example.com</code>) when you run the command.

Some features (like Seer) may not be available on self-hosted instances. You can disable specific skills to prevent unsupported tools from being exposed:

npx @sentry/mcp-server@latest --access-token=TOKEN --host=sentry.example.com --disable-skills=seer

Environment Variables

SENTRY_ACCESS_TOKEN=         # Required: Your Sentry auth token

# LLM Provider Configuration (required for AI-powered search tools)
EMBEDDED_AGENT_PROVIDER=     # Required: 'openai' or 'anthropic'
OPENAI_API_KEY=              # Required if using OpenAI
ANTHROPIC_API_KEY=           # Required if using Anthropic

# Optional overrides
SENTRY_HOST=                 # For self-hosted deployments
MCP_DISABLE_SKILLS=          # Disable specific skills (comma-separated, e.g. 'seer')

Important: Always set EMBEDDED_AGENT_PROVIDER to explicitly specify your LLM provider. Auto-detection based on API keys alone is deprecated and will be removed in a future release. See docs/embedded-agents.md for detailed configuration options.

Example MCP Configuration

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": ["@sentry/mcp-server"],
      "env": {
        "SENTRY_ACCESS_TOKEN": "your-token",
        "EMBEDDED_AGENT_PROVIDER": "openai",
        "OPENAI_API_KEY": "sk-..."
      }
    }
  }
}

If you leave the host variable unset, the CLI automatically targets the Sentry SaaS service. Only set the override when you operate self-hosted Sentry.

For self-hosted instances that don't support Seer:

{
  "mcpServers": {
    "sentry": {
      "command": "npx",
      "args": ["@sentry/mcp-server"],
      "env": {
        "SENTRY_ACCESS_TOKEN": "your-token",
        "SENTRY_HOST": "sentry.example.com",
        "MCP_DISABLE_SKILLS": "seer"
      }
    }
  }
}

MCP Inspector

MCP includes an Inspector, to easily test the service:

pnpm inspector

Enter the MCP server URL (http://localhost:5173) and hit connect. This should trigger the authentication flow for you.

Note: If you have issues with your OAuth flow when accessing the inspector on 127.0.0.1, try using localhost instead by visiting http://localhost:6274.

Local Development

To contribute changes, you'll need to set up your local environment:

  1. Set up environment and agent skills:

    make setup-env  # Creates .env files and installs shared agent skills
    

    This also runs npx @sentry/dotagents install to install shared skills from getsentry/skills into .agents/skills/ (symlinked into .claude/skills and .cursor/skills). If you need to update skills later, run it directly:

    npx @sentry/dotagents install
    
  2. Create an OAuth App in Sentry (Settings => API => Applications):

    • Homepage URL: http://localhost:5173
    • Authorized Redirect URIs: http://localhost:5173/oauth/callback
    • Note your Client ID and generate a Client secret
  3. Configure your credentials:

    • Edit .env in the root directory and add your OPENAI_API_KEY
    • Edit packages/mcp-cloudflare/.env and add:
      • SENTRY_CLIENT_ID=your_development_sentry_client_id
      • SENTRY_CLIENT_SECRET=your_development_sentry_client_secret
      • COOKIE_SECRET=my-super-secret-cookie
  4. Start the development server:

    pnpm dev
    

Verify

Run the server locally to make it available at http://localhost:5173

pnpm dev

To test the local server, enter http://localhost:5173/mcp into Inspector and hit connect. Once you follow the prompts, you'll be able to "List Tools".

Tests

There are three test suites included: unit tests, evaluations, and manual testing.

Unit tests can be run using:

pnpm test

Evaluations require a .env file in the project root with some config:

# .env (in project root)
OPENAI_API_KEY=  # Also required for AI-powered search tools in production

Note: The root .env file provides defaults for all packages. Individual packages can have their own .env files to override these defaults during development.

Once that's done you can run them using:

pnpm eval

Manual testing (preferred for testing MCP changes):

# Test with local dev server (default: http://localhost:5173)
pnpm -w run cli "who am I?"

# Test agent mode (use_sentry tool only)
pnpm -w run cli --agent "who am I?"

# Test against production
pnpm -w run cli --mcp-host=https://mcp.sentry.dev "query"

# Test with local stdio mode (requires SENTRY_ACCESS_TOKEN)
pnpm -w run cli --access-token=TOKEN "query"

Note: The CLI defaults to http://localhost:5173. Override with --mcp-host or set MCP_URL environment variable.

Comprehensive testing playbooks:

  • Stdio testing: See docs/testing-stdio.md for complete guide on building, running, and testing the stdio implementation (IDEs, MCP Inspector)
  • Remote testing: See docs/testing-remote.md for complete guide on testing the remote server (OAuth, web UI, CLI client)

Development Notes

Automated Code Review

This repository uses automated code review tools (like Cursor BugBot) to help identify potential issues in pull requests. These tools provide helpful feedback and suggestions, but we do not recommend making these checks required as the accuracy is still evolving and can produce false positives.

The automated reviews should be treated as:

  • Helpful suggestions to consider during code review
  • Starting points for discussion and improvement
  • Not blocking requirements for merging PRs
  • Not replacements for human code review

When addressing automated feedback, focus on the underlying concerns rather than strictly following every suggestion.

Contributor Documentation

Looking to contribute or explore the full documentation map? See CLAUDE.md (also available as AGENTS.md) for contributor workflows and the complete docs index. The docs/ folder contains the per-topic guides and tool-integrated .md files.

FAQ

What is the Sentry MCP server?
Sentry is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
How do MCP servers relate to agent skills?
Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
How are reviews shown for Sentry?
This profile displays 40 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.5 out of 5—verify behavior in your own environment before production use.

Use Cases

Extended AI Capabilities

Add new capabilities to Claude beyond text generation

Example

Access external data sources, execute code, interact with tools and services

Transform Claude from chatbot to action-taking agent

Context Enhancement

Provide Claude with access to relevant context and data

Example

Load project documentation, access knowledge bases, query databases

Get more accurate, context-aware responses

Workflow Automation

Automate multi-step workflows combining AI and external tools

Example

Research → Summarize → Create document → Send notification

Complete complex tasks end-to-end without manual steps

Implementation Guide

Prerequisites

  • Claude Desktop 0.7.0+ or Cursor IDE with MCP support
  • Basic understanding of MCP architecture and capabilities
  • Access credentials for integrated services (if required)
  • Willingness to experiment and iterate on configuration

Time Estimate

15-60 minutes depending on server complexity

Installation Steps

  1. 1.Install MCP server: npm install -g [package-name] or via GitHub
  2. 2.Add server configuration to ~/.claude/mcp.json
  3. 3.Provide required credentials and configuration
  4. 4.Restart Claude Desktop to load new server
  5. 5.Test basic functionality with simple prompts
  6. 6.Explore capabilities and experiment with use cases
  7. 7.Document successful patterns for reuse

Troubleshooting

  • MCP server not loading: Check config syntax, verify installation
  • Connection errors: Check network, firewall, credentials
  • Feature not working: Read server docs, check required parameters
  • Performance issues: Monitor resource usage, check for network latency
  • Conflicts with other servers: Check port assignments, namespace collisions

Best Practices

✓ Do

  • +Read server documentation thoroughly before setup
  • +Start with simple use cases to validate functionality
  • +Test in non-production environment first
  • +Monitor resource usage and performance
  • +Keep servers updated for bug fixes and new features
  • +Document configuration for team members
  • +Use environment variables for sensitive configuration

✗ Don't

  • Don't grant overly permissive access to MCP servers
  • Don't skip reading security considerations in docs
  • Don't expose sensitive data without proper controls
  • Don't run untrusted MCP servers without code review
  • Don't ignore error messages—investigate root cause

💡 Pro Tips

  • Combine multiple MCP servers for powerful workflows
  • Create custom MCP servers for your specific needs
  • Share successful configurations with team
  • Use MCP inspector for debugging
  • Join MCP community for tips and troubleshooting

Technical Details

Architecture

Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.

Protocols

  • Model Context Protocol (MCP)
  • JSON-RPC 2.0
  • stdio or HTTP transport

Compatibility

  • Claude Desktop
  • Cursor IDE
  • Custom MCP clients

When to Use This

✓ Use When

Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.

✗ Avoid When

Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.

Integration

  • Tool composition: Chain multiple MCP tools in workflows
  • Context augmentation: Provide AI with relevant external data
  • Action delegation: Let AI execute tasks on external systems
  • Bidirectional sync: Keep AI context and external systems in sync

Discussion

Product Hunt–style comments (not star reviews)
  • No comments yet — start the thread.

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Ratings

4.540 reviews
  • Xiao Patel· Dec 24, 2024

    Sentry reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Lucas Jain· Dec 8, 2024

    We wired Sentry into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

  • Omar Srinivasan· Dec 8, 2024

    According to our notes, Sentry benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.

  • Dhruvi Jain· Dec 4, 2024

    Useful MCP listing: Sentry is the kind of server we cite when onboarding engineers to host + tool permissions.

  • Sofia Johnson· Dec 4, 2024

    I recommend Sentry for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.

  • Omar Iyer· Nov 27, 2024

    Sentry has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.

  • Oshnikdeep· Nov 23, 2024

    We evaluated Sentry against two servers with overlapping tools; this profile had the clearer scope statement.

  • Mateo Robinson· Nov 23, 2024

    Strong directory entry: Sentry surfaces stars and publisher context so we could sanity-check maintenance before adopting.

  • Rahul Santra· Nov 3, 2024

    Sentry reduced integration guesswork — categories and install configs on the listing matched the upstream repo.

  • Pratham Ware· Oct 22, 2024

    We wired Sentry into a staging workspace; the listing’s GitHub and npm pointers saved time versus hunting across READMEs.

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