# DeployAngel

Monitors production rollouts and feeds failure diagnostics directly to your coding agent.

- MCP server · `type=mcp` · Claude Code, Codex, Cursor, GitHub Copilot, Windsurf (Devin Desktop), Cline, Zed, Gemini CLI
- Author: DeployAngel
- License: MIT · policy=link-only · description only; the file stays with the author
- Source: https://github.com/DeployAngel/deployangel-python
- Updated: 2026-10-21
- HTML: https://affpapa.org/en/nevibecode/mcp/deployangel

## What it does

The MCP integration links your coding assistant with the DeployAngel monitoring service. The runtime agent collects metrics once per minute from web processes: HTTP status distributions, route latency histograms, unhandled exceptions, and background task queues such as Celery or RQ. This telemetry helps the platform determine whether a new version is cleared or failing.

Sensitive information remains private. The library excludes request bodies, URL arguments, headers, cookies, SQL queries, and logs, sending sanitized traces and route patterns instead. Telemetry streams via a background thread to hosted infrastructure on DigitalOcean in the US. If the remote collector becomes unreachable, an in-memory buffer holds up to 10 compressed packages before dropping older records.

You need a valid `DEPLOYANGEL_API_TOKEN` to run the tool. For production monitoring, the companion package expects Python 3.10 or higher alongside Django 4.2+, FastAPI 0.100+, Starlette 0.27+, or Flask 2.3+.

## Requirements
- Needs: Python and uv (commands run via uvx)
- Environment variables: `DEPLOYANGEL_API_TOKEN` — keys and tokens: ask the human for the values, never commit them or put them in shared files.

## Install

### Claude Code (native platform)

**Platform CLI**
```bash
claude mcp add deployangel -e DEPLOYANGEL_API_TOKEN=<DEPLOYANGEL_API_TOKEN> -- uvx deployangel
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from deployangel <command>

### Codex

**Platform CLI**
```bash
codex mcp add deployangel --env DEPLOYANGEL_API_TOKEN=<DEPLOYANGEL_API_TOKEN> -- uvx deployangel
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from deployangel <command>

### Cursor

**Config file**
File `.cursor/mcp.json`:
```json
{
    "mcpServers": {
        "deployangel": {
            "command": "uvx",
            "args": [
                "deployangel"
            ],
            "env": {
                "DEPLOYANGEL_API_TOKEN": "<DEPLOYANGEL_API_TOKEN>"
            }
        }
    }
}
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from deployangel <command>

### GitHub Copilot

**Config file**
File `.vscode/mcp.json`:
```json
{
    "servers": {
        "deployangel": {
            "type": "stdio",
            "command": "uvx",
            "args": [
                "deployangel"
            ],
            "env": {
                "DEPLOYANGEL_API_TOKEN": "<DEPLOYANGEL_API_TOKEN>"
            }
        }
    }
}
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from deployangel <command>

### Gemini CLI

**Platform CLI**
```bash
gemini mcp add -e DEPLOYANGEL_API_TOKEN=<DEPLOYANGEL_API_TOKEN> deployangel uvx deployangel
```
Replace <…> with your own values. uvx runs the command named after the package; if the README names another one, use uvx --from deployangel <command>

## When to use it
- verify whether a production release passed all stability checks
- inspect exception traces and error spikes after a deployment
- track stuck background workers across Celery or RQ queues
- map runtime failures to specific commit revisions

## Example prompts
- Check if production has cleared our latest release
- Retrieve unhandled exceptions reported for the current FastAPI revision
- Inspect failed Celery jobs and queue latency since the last deploy

## Similar
- [django-celery](https://affpapa.org/en/nevibecode/skills/django-celery.md) — skill · 4600 installs
- [django-celery-expert](https://affpapa.org/en/nevibecode/skills/django-celery-expert.md) — skill · 1100 installs
- [django-python](https://affpapa.org/en/nevibecode/skills/django-python.md) — skill · 933 installs
- [celery](https://affpapa.org/en/nevibecode/skills/celery.md) — skill · 454 installs
- [django-pro](https://affpapa.org/en/nevibecode/skills/django-pro.md) — skill · 103 installs
- [sentry-python-sdk](https://affpapa.org/en/nevibecode/skills/sentry-python-sdk.md) — skill · 2600 installs

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