Browser Use
Lets AI do web tasks for you, such as filling forms, searching and extracting data, in the maker’s cloud browsers or on your computer.
Description from the maker (original English)
Turn any website or API into structured JSON with LLM-authored declarative scraping configs.
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.
FITTER_PLUGINSYou need
Which app do you use?
Pick your app above. We remember it, so next time the button says “Install in …”.
Download the maker’s .mcpb file
The link goes to the maker’s own file; we do not host or repackage it. Open the file and Claude Desktop installs it.
Run this in your terminal
claude mcp add fitter --env FITTER_PLUGINS=your-value -- docker run -i --rm -e FITTER_PLUGINS ghcr.io/pxyup/fitter-mcp:v1.8.3Fill in the empty values before running it.
Paste into .cursor/mcp.json in your project, or ~/.cursor/mcp.json
{
"mcpServers": {
"fitter": {
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"FITTER_PLUGINS",
"ghcr.io/pxyup/fitter-mcp:v1.8.3"
],
"env": {
"FITTER_PLUGINS": "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": {
"fitter": {
"type": "stdio",
"command": "docker",
"args": [
"run",
"-i",
"--rm",
"-e",
"FITTER_PLUGINS",
"ghcr.io/pxyup/fitter-mcp:v1.8.3"
],
"env": {
"FITTER_PLUGINS": "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.
Your choice stays on this device in a cookie; no account needed.
FITTER_PLUGINS envThese 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 8, 2026
The registry name was verified with the GitHub account PxyUp, which owns the repo
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.
The registry does not list a required key, though some servers ask you to sign in on first use.
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 (1.8.3) reached the registry 2 months ago.
Apps that support MCP, such as Claude Desktop, Claude Code, Cursor and VS Code. You also need MCPB, Docker on your computer.
In the same category: Browser Use, Chrome DevTools MCP, MCP Server Browser
Servers in the same category from other makers, sorted by stars.
Reading a card:↓ Downloads per weeki★ GitHub starsiThree pillars: reviews · checks · usageiUpdated = latest version published to the registryi
Lets AI do web tasks for you, such as filling forms, searching and extracting data, in the maker’s cloud browsers or on your computer.
Lets AI use Chrome’s developer tools to inspect pages, find what makes them slow and spot errors. Made by the Chrome DevTools team.
Copied from the maker’s repo as written. PopNx MCP has not reviewed or endorsed it. HTML, images and scripts are removed. Links lead off this site.
Fitter turns any website or API into structured JSON — declaratively. One JSON/YAML config describes where the data lives (HTTP request, headless browser, file, static value) and what to extract (JSON paths, CSS selectors, XPath). No code, no brittle scraping scripts.
🚀 [Try it in your browser](https://pxyup.github.io/fitter/) — the real engine compiled to WebAssembly: live examples, a visual config builder, no install.
Because configs are plain data, LLMs can author them. The built-in MCP server lets Claude Code, Claude Desktop, or any MCP client write and run scraping pipelines on your machine, on demand:
*"Get the top 5 HackerNews stories with titles and scores"* → the model authors a fitter config, validates it, runs it locally, and gets clean JSON back.
One engine, five ways to use it:
| 🤖 Fitter MCP | MCP server exposing fitter to Claude Code, Claude Desktop and any MCP client |
| 🧠 Fitter Agent | AI-powered CLI: natural language → config → executed result |
| 🖥 Fitter CLI | run configs locally for test/debug/home usage |
| 📦 Fitter Lib | embed the engine in your own Go program |
| ⚙️ Fitter | long-running service mode with scheduling & notifications |
Why fitter for AI agents?
Fitter MCP is a Model Context Protocol server (stdio transport) which lets any MCP client — Claude Code, Claude Desktop, IDE assistants, custom agents — run Fitter configs and get structured JSON back.
Download fitter-mcp- - .mcpb from the release page and open it — Claude Desktop installs the server automatically.
# 1. get the binary: download fitter_mcp_<version>-<os>-<arch> from the release page
go build -o fitter_mcp ./cmd/mcp
claude mcp add fitter -s user -- "$(pwd)/fitter_mcp"
Then just ask:
*Get the top 5 HackerNews stories with titles and scores using fitter*
The model calls fitter_config_reference, authors a config, optionally checks it with fitter_validate_config and executes it via fitter_run — all data fetching happens locally on your machine. For a ready-made pipeline try examples/config_morning_briefing.json:
*Run examples/config_morning_briefing.json with fitter and give me the briefing*
{
"mcpServers": { "fitter": { "command": "/path/to/fitter_mcp" } } }
The .mcpb bundle and native binary ship without browsers: HTTP, static and file connectors work out of the box, but browser configs (the playwright connector) need Playwright's browsers. A few ways to get them:
"install": true in the playwright connector — fitter downloads the driver + browser matching its built-in playwright-go version on first use (one-time, cached), so no separate install step is needed.playwright-go version fitter is built against (check go.mod, currently v0.6100.0): go run github.com/mxschmitt/playwright-go/cmd/playwright@v0.6100.0 install
The version must match `go.mod` exactly — `playwright-go` refuses to run against a mismatched driver. Then run configs without `"install": true`.
ghcr.io/pxyup/fitter-mcp:playwright image, which bundles Chromium, Firefox and WebKit preinstalled (no "install": true needed).| Tool | Description |
|---|---|
fitter_run | Run a Fitter config passed inline (JSON or YAML string) and return the extracted data as JSON. Accepts an optional input value available in the config via {{{FromInput=.}}} / {{{FromInput=json.path}}} |
fitter_run_file | Same as fitter_run but reads the config from a local .json/.yaml file |
fitter_run_url | Same as fitter_run but downloads the config from an HTTP(S) URL, e.g. a raw GitHub link |
fitter_inspect_url | Fetch a URL and return a compact structure outline + candidate selectors/paths (gjson paths for JSON; repeated-element/list-row selectors for HTML) so the model authors a config first-try instead of guessing selectors and getting nulls. Detects client-rendered SPAs and can render them in a headless browser. Read-only — does not extract |
fitter_validate_config | Validate a config without executing it (structure, response_type, connector data source, model). Useful while iterating on a config |
fitter_config_reference | Return a condensed reference of the whole config format (connectors, parsers, model/field schema, placeholders, notifiers, references, limits) with working examples, so the model can author configs without external docs |
The reference is also exposed as MCP resource fitter://config-reference for clients that support resources.
The config format is exactly the same as for Fitter_CLI: a top-level object with item (required), limits and references. Notifiers work too (the result is additionally pushed to http/telegram/redis/file/console); trigger_config and http_server are service-mode only and are ignored in MCP calls.
By default fitter_mcp talks stdio. Pass --http to serve the streamable HTTP transport instead — for a shared team server, a container, or any remote deployment:
# serve MCP at http://<host>:8080/mcp (health probe at /healthz)
FITTER_MCP_AUTH_TOKEN=my-secret fitter_mcp --http :8080
claude mcp add --transport http fitter http://localhost:8080/mcp --header "Authorization: Bearer my-secret"
--http (env FITTER_MCP_HTTP_ADDR) — listen address; stdio mode when emptyFITTER_MCP_AUTH_TOKEN — when set, every /mcp request must send Authorization: Bearer ; without it the endpoint is unauthenticated, so bind to localhost or put it behind a proxy--stateless (env FITTER_MCP_STATELESS=true) — no per-session state, so replicas can sit behind a load balancer without sticky sessions--metrics-addr (env FITTER_MCP_METRICS_ADDR) — serve Prometheus metrics on a separate address (e.g. 127.0.0.1:9091); works in stdio mode too; empty disables. Exposes MCP request counts by method, tool call durations by tool and outcome, and outbound HTTP request durations by host, method and status. The endpoint is unauthenticated — bind it to localhost or a private interfaceThe server shuts down gracefully on SIGINT/SIGTERM.
A slim multi-arch image (linux/amd64 + linux/arm64) ships with every release:
# hosted HTTP mode
docker run --rm -p 8080:8080 \ -e FITTER_MCP_HTTP_ADDR=:8080 \ -e FITTER_MCP_AUTH_TOKEN=my-secret \ ghcr.io/pxyup/fitter-mcp:latest
claude mcp add fitter -s user -- docker run --rm -i ghcr.io/pxyup/fitter-mcp:latest
The slim image contains only the fitter binary and CA certificates: server/static/file connectors work, browser connectors (chromium/docker/playwright) do not.
For browser-based configs use the playwright variant, which bundles Playwright with Chromium, Firefox and WebKit (matched to the playwright-go version fitter is built against, so no "install": true is needed in configs):
docker run --rm -i ghcr.io/pxyup/fitter-mcp:playwright # stdio mode
It is built from Dockerfile.mcp-playwright; build with --build-arg PLAYWRIGHT_BROWSERS=chromium for a smaller Chromium-only image.
Both images ship fitter_cli, so the one-time OAuth2 login can run inside the container. Store the token on a volume mounted at /tokens (pre-created writable in the image) and share it with the MCP server:
# one-time login, device flow: no ports needed — open the printed url on any device
docker run --rm -it -v fitter-tokens:/tokens --entrypoint fitter_cli \ ghcr.io/pxyup/fitter-mcp:latest \ auth --provider github --client-id --client-secret --token-file /tokens/github.json
docker run --rm -it -p 8988:8988 -e FITTER_AUTH_LISTEN=0.0.0.0 \ -v fitter-tokens:/tokens --entrypoint fitter_cli ghcr.io/pxyup/fitter-mcp:latest \ auth --provider github --client-id --client-secret --token-file /tokens/github.json
docker run --rm -i -v fitter-tokens:/tokens ghcr.io/pxyup/fitter-mcp:latest
docker run --rm -p 8080:8080 -v fitter-tokens:/tokens \ -e FITTER_MCP_HTTP_ADDR=:8080 \ -e FITTER_MCP_AUTH_TOKEN=my-secret \ ghcr.io/pxyup/fitter-mcp:latest
Note: 8988 is only for the one-time browser-flow login; the MCP server itself needs no port in stdio mode and only 8080 in hosted HTTP mode.
Browser sessions need the playwright image (the slim one has no browsers). The one-time headed login needs a display, so run it on the host, then bind-mount the session dir into the container (the image pre-creates a writable /sessions):
# on the host: log in once, save the session
fitter_cli browser-login --url https://example.com/login --storage-state ~/.fitter/sessions/example.json
docker run --rm -i -v ~/.fitter/sessions:/sessions ghcr.io/pxyup/fitter-mcp:playwright
Use a bind mount (not
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