Unofficial Sentry MCP that provide unavailable tools on the official MCP.
Sentrycompanionmcp MCP server is a locally run integration for AI assistants that speak the Model Context Protocol. Unofficial Sentry MCP that provide unavailable tools on the official MCP.
Once Sentrycompanionmcp is connected, these are the calls the assistant has available:
get_release_health — CFU, crash-free sessions, session and user counts for a releaseget_release_adoption — Session-based and user-based adoption %compare_releases — Side-by-side CFU comparison between two releasesget_release_new_issues — Issues first seen in a specific releaseget_release_regressed_issues — Issues that regressed in a specific releaseget_release_deploys — Deployment history for a releaseadd_issue_comment — Post a comment on a Sentry issueYou will need 4 environment variables: SENTRY_PERSONAL_TOKEN, SENTRY_ORG, SENTRY_PROJECT, SENTRY_BASE_URL. The server will not start without them, which is usually why the tools fail to appear on a first run. Keep credentials in your client's env block or a secrets manager rather than in a file you might commit.
Installation goes through your MCP client rather than a global install: point it at sentry-companion-mcp on PyPI and it is fetched when the client starts. The copy-paste blocks for Claude Desktop, Claude Code and Cursor are further down this page.
This sits in the monitoring and observability group, where several servers overlap in what they claim to do but differ sharply once you actually set them up. Sentrycompanionmcp's toolset — get_release_health, get_release_adoption, compare_releases and 4 more — is a fair guide to whether it matches your workflow. It is maintained by agimaulana; worth a glance at recent repository activity before you build anything load-bearing on it.
This entry was verified against Sentrycompanionmcp's own documentation before publication; SyncDev keeps the directory reviewed rather than auto-generated.
| Tool | What it does |
|---|---|
| get_release_health | CFU, crash-free sessions, session and user counts for a release |
| get_release_adoption | Session-based and user-based adoption % |
| compare_releases | Side-by-side CFU comparison between two releases |
| get_release_new_issues | Issues first seen in a specific release |
| get_release_regressed_issues | Issues that regressed in a specific release |
| get_release_deploys | Deployment history for a release |
| add_issue_comment | Post a comment on a Sentry issue |
Or add manually to your `.mcp.json`:
```json
{
"mcpServers": {
"sentry-companion": {
"type": "stdio",
"command": "uvx",
"args": ["sentry-companion-mcp"],
"env": {
"SENTRY_PERSONAL_TOKEN": "your_token",
"SENTRY_ORG": "your-org-slug",
"SENTRY_PROJECT": "your-project-id",
"SENTRY_BASE_URL": "https://us.sentry.io"
}
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| SENTRY_PERSONAL_TOKEN | Credential the server authenticates with. | Yes |
| SENTRY_ORG | Configuration value read at startup. | Optional |
| SENTRY_PROJECT | Configuration value read at startup. | Optional |
| SENTRY_BASE_URL | Endpoint or connection string the server talks to. | Yes |
Give your coding agent the full DevTools toolbox: traces, network, console, heap snapshots and Lighthouse.
Dashboards, Prometheus and Loki queries, incidents and alerts — observability by conversation.
Errors with full context — stack traces, issue triage and AI-powered root-cause analysis from Sentry's server.
Enables enhanced web research capabilities for large language models through intelligent search queuing and advanced content extraction.
Automates browser interactions and enables Large Language Models (LLMs) to interact with web pages through Playwright and Chrome DevTools Protocol
Guides tool usage by providing recommendations for MCP tools at each problem-solving stage.