Fantoma MCP server

Fantoma

A stealth browser for AI agents. It reads pages through the accessibility tree, so small local models can drive it. Fantoma sees a page the way a screen reader does, as structured text rather than screenshots, so the model gets words instead of pixels.

<!-- mcp-name: io.github.Huzy85/fantoma --> mcp-name: io.github.Huzy85/fantoma

No vision model, no pixel coordinates, no mouse telemetry. A step costs about 500 tokens instead of the 1,000-5,000 a screenshot costs, which is what makes a small model on your own hardware practical.

What that buys you, and what it does not. Reading pages works on a small local model. In a 31-site run across 9 models, a 35B local MoE scored 30/31, matching the best cheap API models and beating most of them. That run included sites that block conventional scrapers. Multi-step interaction is a different story: the same local model completed 1 of 6 login and form flows, while a frontier API model completed all of them. So extraction on your own hardware is real. For interaction, Fantoma now does the common steps itself (log in, search, pick an option, type into a field, tick a box, add a named item to the cart) and checks the page afterwards, so the model is only asked about what code cannot settle. On GitHub's machines with qwen2.5 7B and 14B, those steps pass with no model action at all (details). Open-ended multi-page flows still need a stronger model. Several jobs need no model at all:

pip install fantoma
fantoma read https://news.ycombinator.com     # any page as clean Markdown, no LLM

from fantoma import Fantoma, Agent

# Read: clean Markdown + every link. No LLM. Hidden text removed.
browser = Fantoma()
browser.start()
page = browser.read("https://example.com/pricing")
page["markdown"], page["links"], page["blocked"]   # blocked: "bot_challenge", "login_wall", ... or ""

# Login: code fills the form. No LLM.
browser.login("https://github.com/login", email="me@example.com", password="...")
browser.stop()

# Tool API: drive it step by step from your own agent loop
browser = Fantoma()
state = browser.start("https://news.ycombinator.com")
# state["aria_tree"] → feed to your LLM, get back an action
result = browser.click(3)
browser.stop()

# Agent: describe a task, a local model does it
agent = Agent(llm_url="http://localhost:11434/v1")      # Ollama, llama.cpp, vLLM, or any OpenAI-compatible API
result = agent.run("Go to github.com/trending and tell me the top repo")

At a glance

Shortened. The full README is on GitHub.