GitHub
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
Description from the maker (original English)
Wait for production to clear each deploy, and read the evidence when a release fails.
These three are never merged into one score: opinions, measurements and popularity are different things.
We have not written a plain-language summary for this one yet. Below are the facts we read from the registry.
Pick the AI app you use, then copy the settings.
DEPLOYANGEL_API_TOKENValues marked secret are keys to your account. Never share them or paste them into a chat.
You need
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Pick your app above. We remember it, so next time the button says “Install in …”.
Open Claude Desktop → Settings → Developer → Edit Config, and paste this into claude_desktop_config.json
{
"mcpServers": {
"deployangel": {
"command": "uvx",
"args": [
"deployangel",
"mcp"
],
"env": {
"DEPLOYANGEL_API_TOKEN": "your-value"
}
}
}
}Run this in your terminal
claude mcp add deployangel --env DEPLOYANGEL_API_TOKEN=your-value -- uvx deployangel mcpFill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"deployangel": {
"command": "uvx",
"args": [
"deployangel",
"mcp"
],
"env": {
"DEPLOYANGEL_API_TOKEN": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
Paste into .vscode/mcp.json in your project
{
"servers": {
"deployangel": {
"type": "stdio",
"command": "uvx",
"args": [
"deployangel",
"mcp"
],
"env": {
"DEPLOYANGEL_API_TOKEN": "your-value"
}
}
}
}Your browser asks to open the app, then the app shows the settings and asks you to confirm. Key values are left empty for you to fill in.
This one runs on your computer, and ChatGPT can only connect to servers on the internet. Pick another app.
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These settings are generated from the maker’s registry entry. If the README says otherwise, follow the README.
Runs on your computer; the tool list appears in your AI app after you install it.
We check information anyone can see; we have not read the code. Checks that do not apply are left out of the count. · Checked Nov 6, 2026
The registry name was verified against the domain deployangel.com (the registry checks domain ownership)
We have not read this repo from GitHub yet
We have not read this repo from GitHub yet
This one runs on your computer; there is no internet endpoint
Runs on your computer and uses local settings instead
Runs on your computer; we can only read tool lists from internet endpoints
Compared with every active server in this category in the registry. Stars count only when the publisher owns the repo.
It passed 1 of 1 applicable basic checks. We have not read the code. Before adding a key or token, see what access the server asks for and use a key with only the permissions you need.
You need to fill in: DEPLOYANGEL_API_TOKEN
It runs on your computer. Your AI app starts it when needed, and data does not pass through the maker’s servers (unless it calls an online service itself).
The GitHub repo last changed –, and the latest version (0.1.29) reached the registry today.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need Python (uv) on your computer.
In the same category: GitHub, Desktop Commander, Cloudflare MCP
Servers in the same category from other makers, sorted by stars.
Reading a card:★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
GitHub’s official server lets AI read and manage code, issues and pull requests in your account. Use GitHub’s hosted version or run it locally with Docker.
MCP server for terminal commands, file operations, and process management
Cloudflare MCP servers
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The DeployAngel agent for Django, FastAPI, Starlette, and Flask, with Celery and RQ jobs. It watches what your app does in production and reports one small aggregated payload per process per minute. DeployAngel uses that to verify every deployment, and to tell you (or your coding agent) when a release is cleared and you can stop watching it.
Requires Python 3.10 or later. The package has no dependencies of its own.
pip install deployangel
Set this in production, for every process (web servers and job workers alike):
DEPLOYANGEL_TOKEN=da_live_... # an ingestion token with the telemetry scope
On Heroku, the DeployAngel add-on sets it for you.
Supports Django 4.2 or later.
# settings.py
INSTALLED_APPS = [
"deployangel.django", ]
That's all. The app puts DeployAngel's middleware at the top of MIDDLEWARE (list it yourself to place it), records requests under WSGI and ASGI, and instruments Celery when it's installed. Reporting is on when DEBUG is off.
Supports FastAPI 0.100 and Starlette 0.27 or later.
import deployangel.fastapi
app = FastAPI() deployangel.fastapi.init(app) # deployangel.starlette.init(app) for Starlette
Call it where the app is created, before it serves requests.
Supports Flask 2.3 or later.
import deployangel.flask
app = Flask(name) deployangel.flask.init_app(app)
Supports Celery 5.3 or later. In Django it's automatic. Elsewhere, call init where the Celery app is created, so worker processes start the agent:
import deployangel.celery
app = Celery("shop") deployangel.celery.init(app)
Supports RQ 1.16 or later. Run workers with DeployAngel's worker class:
rq worker -w deployangel.rq.Worker # or deployangel.rq.SimpleWorker
python manage.py rqworker --worker-class deployangel.rq.Worker # django-rq
RQ runs each job in a forked process that exits as soon as the job ends, so the worker records each job after it finishes, from what RQ saved in Redis. That includes a failed job's traceback, but not the exception of an attempt RQ retries.
The agent must know which release it's running. It finds it in this order:
DEPLOYANGEL_REVISION (the commit SHA) and DEPLOYANGEL_RELEASE_VERSION, if you set themheroku labs:enable runtime-dyno-metadata and heroku labs:enable runtime-dyno-build-metadata; both take effect on the next deployKAMAL_VERSIONRENDER_GIT_COMMITFLY_IMAGE_REF. Fly.io sets no commit, so pass one in for commit-level change tracking (ENV DEPLOYANGEL_REVISION=$GIT_SHA in the Dockerfile, with --build-arg GIT_SHA=$(git rev-parse HEAD))RAILWAY_GIT_COMMIT_SHA, or RAILWAY_DEPLOYMENT_IDSOURCE_COMMIT. Dokku: GIT_REVREVISION file in the app's rootHEAD names, read from .git in the app's root or up to 3 directories above it, without running git. This covers servers deployed by git pull, Fabric, or Ansiblecode: and the first 12 characters of the hash of the file digests the agent already sends (below), with no commit. The same code gives the same fingerprint, so a redeploy of unchanged code isn't a new release. It's computed in the background when the reporter starts, not at boot, and needs file digests on. Without a commit DeployAngel still shows which files changed in a release, but not its commits and pull requests, so the agent logs a warning: set DEPLOYANGEL_REVISION when you canFor other Docker deploys, bake the commit into the image: deployangel install docker adds ARG GIT_SHA and ENV DEPLOYANGEL_REVISION=$GIT_SHA to the end of your Dockerfile, where it doesn't invalidate cached layers, and you build with --build-arg GIT_SHA=$(git rev-parse HEAD). On DigitalOcean App Platform, set DEPLOYANGEL_REVISION: ${_self.COMMIT_HASH} in the app spec.
GET /users/ /, GET /items/{item_id}), never the raw path. At most 100 routes are sent per payload; the rest are folded into __other__.deployangel_published_at, the time they were sent.Payment failed for Jane Doe is sent as it is. If your app's messages might hold personal or health data, turn messages off.DEPLOYANGEL_FILE_DIGESTS=false.It does not send request bodies, parameters, headers, cookies, SQL, logs, or user data.
enable-threads = true).python bench/overhead.py measures this. On an Apple M-series laptop with Python 3.14, recording a request adds about 1.7 µs, and with every route, job, checkpoint, and exception list at its cap the agent holds under 1 MB (0.73 MB at peak), including an hour (60) of unsent minutes.
Environment variables are enough for most apps. In Django, settings go in a DEPLOYANGEL dict; elsewhere, call deployangel.configure before init:
# settings.py
DEPLOYANGEL = { "environments": ["production", "staging"], # default: production only }
deployangel.configure(environments=["production", "staging"])
The agent reports in the environments listed. Python frameworks don't name an environment, so it's DEPLOYANGEL_ENVIRONMENT if set, otherwise development when the framework's debug mode is on and production when it's off. DEPLOYANGEL_ENABLED=true|false forces reporting on or off anywhere. DEPLOYANGEL_URL overrides the API endpoint (default https://api.deployangel.com).
File paths are relative to the app's root, the working directory by default (on Heroku and in most containers, the repository's root). Set DEPLOYANGEL_ROOT if your processes start somewhere else.
Critical flows (for example sign-up or password reset) are always listed in clearance reports:
DEPLOYANGEL = {
"critical_flows": {"password_reset": ["POST /password-reset/", "job:accounts.tasks.send_reset_email"]}, }
Health checks aren't recorded: load balancers and uptime monitors call them all the time and they always answer fast, so they would make your app look busier and healthier than its real pages. The agent recognizes django-health-check, django-alive, and django-watchman wherever they're mounted, and any route at a conventional path (/up, /health, /healthz, /healthcheck, /health_check, /livez, /readyz, /statusz, /ping, /ht, /alive). If yours is somewhere else, list it the way the dashboard shows it. HEAD requests to it are left out too:
DEPLOYANGEL = {"ignored_routes": ["GET /status/"]}
To send exceptions without any message, only their class, fingerprint, and stack frames:
DEPLOYANGEL = {"exception_messages": False} # or DEPLOYANGEL_EXCEPTION_MESSAGES=false
Grouping, new-exception detection, and verdicts work the same, since the fingerprint never uses the message. You lose the message text in the dashboard, notifications, and AI investigation.
Exceptions your code catches aren't seen. To report one for context (handled exceptions never fail a release):
try:
sync_inventory() except UpstreamError as error: deployangel.notify(error)
DeployAngel expects declared recurring tasks on schedule. It reads Celery Beat's beat_schedule (crontabs, and intervals, which repeat from when Beat starts) in the zone Celery uses, and, when django_celery_beat is installed, its enabled periodic tasks. Solar schedules aren't read, and neither are RQ's schedulers, whose jobs live only in Redis. DeployAngel also learns recurring jobs from their history.
Work that something outside the app starts on a schedule, such as cron, Heroku Scheduler, or a Kubernetes CronJob, is invisible to Celery Beat. Declare it by the name it runs under, with a cron line or words like "every day at 4am", in the server's time zone unless you add one ("0 3 * * * America/New_York"):
DEPLOYANGEL = {
"recurring_jobs": { "shop.tasks.send_receipts": "0 3 * * *", # a Celery or RQ task "manage.py send_invoices": "every day at 4am", # python manage.py send_invoices "nightly import": "30 2 * * *", # deployangel.task("nightly import") }, }
"manage.py " is recorded with no code change. with deployangel.task("nightly import"):
run_import()
@deployangel.task("nightly import") def run_import(): ...
django-crontab's CRONJOBS setting is read automatically when django_crontab is installed: each job is expected on its schedule in the server's zone, and its runs are recorded with no change to the job (a job that calls call_command is recorded as manage.py ).
Errors and latency don't catch work that silently stops happening. Count the business events that matte
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