MCP server for retrieving issues from sentry.io or self-hosted Sentry instances
MCP server for retrieving issues from sentry.io or self-hosted Sentry instances. Exposed over MCP by the mcp mcp server, that capability becomes something an assistant can invoke while it works, not something you go and do afterwards.
Below is a revised and polished version of the README.md for the mcp-sentry-custom project, formatted properly for clarity, correctness, and professionalism. It adheres to standard Markdown conventions and organizes the content into logical sections.
mcp-sentry-custom is a Model Context Protocol (MCP) server designed to retrieve and analyze issues from Sentry.io or self-hosted Sentry instances. This server provides tools to inspect error reports, stack traces, and other debugging information directly from your Sentry account.
Everything the assistant can do here goes through one of these:
Description — Retrieve and analyze a specific Sentry issue by its ID or URLInput — - issue_id_or_url (string): The Sentry issue ID or full URL to analyzeReturns — Detailed issue information, including:Title — Issue IDStatus — LevelTools — 1. get_sentry_issue - Description: Retrieve and analyze a specific Sentry issue by its ID or URL. - Input: - issue_id_or_url (string)Prompts — 1. sentry-issue - Description: Retrieve formatted issue details from Sentry for use in conversation context. - Input: - issue_id_or_urlYou will need 3 environment variables: PYTHONPATH, YOUR_SENTRY_TOKEN, YOUR_SENTRY_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.
Being a remote server, there is no local install. You register the endpoint with your client, authorise it once, and the tools appear.
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. MCP's toolset — Description, Input, Returns and 4 more — is a fair guide to whether it matches your workflow.
We check each listing at SyncDev against the project's documentation before it goes live — if something here drifts out of date, it is a bug worth reporting.
| Tool | What it does |
|---|---|
| Description | Retrieve and analyze a specific Sentry issue by its ID or URL. |
| Input | - issue_id_or_url (string): The Sentry issue ID or full URL to analyze. |
| Returns | Detailed issue information, including: |
| Title | Issue ID |
| Status | Level |
| Tools | 1. **get_sentry_issue** - **Description**: Retrieve and analyze a specific Sentry issue by its ID or URL. - **Input**: - issue_id_or_url (string): The Sentry issue ID or full URL to analyze. - **Returns**: Detailed issue |
| Prompts | 1. **sentry-issue** - **Description**: Retrieve formatted issue details from Sentry for use in conversation context. - **Input**: - issue_id_or_url (string): The Sentry issue ID or URL. - **Returns**: Formatted issue det |
#### Using Docker
```json
{
"mcpServers": {
"sentry": {
"command": "docker",
"args": [
"run", "-i", "--rm",
"mcp/sentry",
"--auth-token", "YOUR_SENTRY_TOKEN",
"--project-slug", "YOUR_PROJECT_SLUG",
"--organization-slug", "YOUR_ORGANIZATION_SLUG",
"--sentry-url", "YOUR_SENTRY_URL"
]
}
}
}Configuration as documented by the project. Restart the client after saving.
| Variable | Description | Required |
|---|---|---|
| PYTHONPATH | Filesystem location the server is allowed to use. | Optional |
| YOUR_SENTRY_TOKEN | Credential the server authenticates with. | Yes |
| YOUR_SENTRY_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.