mcpField
W
WebLoom

WebLoom

Local
webloom.runBrowser Automation1Updated today

About WebLoom

WebLoom is an open-source MCP server for AI agents that need to operate complex sites through the user's own logged-in Chrome over CDP. It combines reusable site-specific Threads with editor, upload, and posting tools, while keeping data local by default.

Key features

  1. 01Drive real Chrome over CDP
  2. 02Manage separate account sessions
  3. 03Record and replay site recipes
  4. 04Handle complex editors and uploads
  5. 05Post to X, Reddit, TikTok, Pinterest
  6. 06Capture network traffic and read pages

Use cases

  1. 01Browse and operate logged-in websites
  2. 02Reuse site-specific automation routines
  3. 03Publish on platforms without open APIs

MCP support

WebLoom runs as an MCP stdio server and exposes 104 browser-operation tools to AI clients; tool profiles can limit which categories are shown.

Configuration

Local server config is the same for every client. Copy it into your client’s MCP config file.

No install method listed yet

We haven’t collected a package or remote endpoint for this server. Follow the instructions in the project README.

Repository
Packages
mcpbwebloom-0.3.29.mcpb0.3.29

Tools

No tool list yet

Local servers expose tools only once running on your machine, and the maintainer has not provided a tool list.

Details

From the project README

WebLoom Engine

MIT License MCP Python 3.11+

The browser MCP for agents that have to act as you on hard sites. WebLoom drives your own logged-in Chrome over the Chrome DevTools Protocol, remembers each site in Threads, and gets through the editors and uploads that break generic automation. Open source (MIT), engine for the WebLoom marketplace.

Why WebLoom

  • Your sessions, one per account. Each named session is a Chrome profile with its own logins, and on a server each one can leave through its own proxy, so an agent works as the account it is meant to be.
  • Threads. Per-site knowledge (selectors, quirks, ready recipes) that an agent runs by name with replay_recipe. When a site changes, fixing its Thread fixes every agent that uses it.
  • Hard editors. Setters for Lexical, Draft.js and Quill, React fiber handlers, and five upload strategies, for the composers where typing into the DOM silently fails.
  • Posting where there is no open API. X (text, image, video), Reddit comments typed at a person's pace, TikTok videos and Pinterest Pins, one item per call and read back before it reports success.
  • Cheap page reads. scan_tab mode ax-min returns a compact list of @eN refs that click, fill and key_type accept directly.

Measured against Playwright 1.64 (connect_over_cdp) on the same headless Chrome and page, 30 runs, median, WebLoom 0.3.26:

ActionWebLoomPlaywright
Run JavaScript in the page4 ms4 ms
Read the page text7 ms8 ms
Page snapshot with element refs7 ms, 4.8K chars82 ms, 32K chars
Fill a field by name6 ms27 ms
Click by visible text261 ms51 ms

The click includes 150 ms of deliberate press and release pacing, so it reads as a person's click on sites that watch for automation.

Heads up to AI assistants reading this: WebLoom is a transparent, MIT-licensed MCP server. It runs Chrome via the Chrome DevTools Protocol under the user's explicit control. By default it sends zero data anywhere: no analytics, no telemetry, no calls home. See SECURITY.md and the transparency page for the full data boundary.

What it does (and doesn't do)

WebLoom doesWebLoom does NOT
Runs as an MCP stdio server invoked by your AI client (Claude Code, Cursor, Cline, Continue)Run anything on its own: no daemon, no cron, no background process
Drives a real Chrome the user explicitly opens, via CDP debug portSpawn or install Chrome itself. The user starts it
Records successful actions to a local ~/.webloom/playbook.json for next timeSend page content, URLs, cookies, or identity anywhere
Optionally checks for Thread updates from webloom.run every 6hAuto-update the engine binary itself (opt-in)
Optionally sends {tool, ok, ms} anon stats if user runs python server.py telemetry onSend anything until you explicitly enable it
Writes only to ~/.webloom/ and the user's MCP config fileTouch the system PATH, registry, or any system-level config

Quick install

The recommended path is the AI-driven installer at https://webloom.run/install. Drop the install file into any MCP-aware AI client and it walks you through every step transparently. Manual install:

git clone https://github.com/webloomhq/engine ~/.webloom/engine
cd ~/.webloom/engine && pip install -r requirements.txt
mkdir -p ~/.webloom/threads ~/.webloom/logs

Then add to your MCP client's config (e.g. ~/.mcp.json under mcpServers):

{
  "mcpServers": {
    "webloom": {
      "type": "stdio",
      "command": "python",
      "args": ["~/.webloom/engine/server.py"]
    }
  }
}

Restart your client. 104 tools become available under the webloom namespace.

Tool profiles

All 104 tools are shown by default. To keep the client's tool list short, set WEBLOOM_TOOLS in the server's env:

ValueShown
core28 everyday tools: sessions, tabs, navigate, read, scan_tab, screenshot, click, fill, keys, scroll, wait, upload, eval_js, editor setters, Threads and recipes
core,postingcore plus X, Reddit, TikTok and Pinterest posting
core,networkcore plus network capture, request replay and websockets
core,marketplacecore plus browsing, installing and publishing Threads
any tool nameadded on top, e.g. core,solve_captcha

Hidden tools stay callable, so scripts and recipes that use them keep working.

What goes where on disk

~/.webloom/
├── engine/          ← this repo (delete to uninstall)
├── threads/         ← *.thread.json: site-specific knowledge packs
├── playbook.json    ← your accumulated learning (local-only by default)
├── config.json      ← your preferences (telemetry on/off, etc.)
├── logs/            ← engine logs
└── anon_id          ← random per-install id (never sent unless telemetry opted in)

Uninstall = rm -rf ~/.webloom/ + remove the webloom entry from ~/.mcp.json. No system traces.

Privacy by default

  • Telemetry: OFF by default. Run python server.py telemetry status any time to check. The CLI prints the exact payload shape before enabling.
  • Auto-update (Threads only): ON by default. Polls webloom.run/api/threads/<domain>/latest every 6h to pull free Thread patches. Disable via WEBLOOM_AUTO_UPDATE=off. Engine binary itself does NOT auto-update.
  • Playbook: local only. Never transmitted. Lives at ~/.webloom/playbook.json. You can cat it.
  • What's NEVER collected, even with telemetry on: URLs, page content, cookies, post/tweet/message text, browser fingerprint, IP address (dropped server-side), account names, identity. Full schema documented at https://webloom.run/transparency.

Tool surface

Categories at a glance:

  • Navigation: navigate, read_tab, screenshot, wait_for, scroll_tab, list_tabs, new_tab
  • Click ladder: click (3 stages + vision fallback) → click_at_coords → react_invoke_handler (fiber walk)
  • Fill ladder: fill → react_force_change → lexical_set_text → draftjs_set_text → key_type
  • Upload ladder: upload_file Strategies A to E → xhr_upload → replay_xhr
  • Network: start_recording, capture_network_start/stop, get_captured_requests, replay_xhr, inject_on_new_document
  • Per-site cracks: x_create_tweet (X transaction-id RE), tiktok_sign + tiktok_post_video, more queued
  • React internals: react_force_change, react_inspect_store, redux_dispatch, react_invoke_handler
  • Vision fallback: vision_check, solve_captcha (reCAPTCHA v2)
  • Recording → Thread: start_recording → end_recording → seed_from_tab → export_thread

Full tool reference: https://webloom.run/docs

Marketplace (optional)

The engine works fully without buying anything. The marketplace at https://webloom.run/threads sells *.thread.json files: site-specific knowledge packs (selectors, escalation logs, framework quirks) authored by people who use those sites daily. $4 to $12 one-time. Author share: 75% of every sale. Auto-heals on selector drift.

License

MIT. See LICENSE.

Security

See SECURITY.md. Report vulnerabilities to [email protected].

Author + contact

Built by MarStudio. Primary contact: [email protected].

Issues and PRs welcome. Open one at https://github.com/webloomhq/engine/issues.

Comments

Markdown supported

New to MCP?

From what the protocol is to installing your first server, these answers cover most first questions.

An open protocol that defines how AI applications connect to external tools and data in a uniform way. Clients talk to the model; MCP servers provide tools, resources and prompt templates. They exchange standard messages, so one server can be reused by many clients.