Browser Intent

Browser Intent

Natural-language layer on top of the low-level browser_* selector tools. Where browser-extract and browser-form-fill compose selector-based primitives (browser_click, browser_fill, browser_snapshot), browser-intent lets the caller say what they want ("Click the login button", "Fill the search box with cats and submit") and delegates execution to page-agent — in-page injected JS that turns the DOM into text and drives an LLM tool-call loop against it.

When to use

• The target element is easier to describe in words than to select reliably (dynamic class names, ambiguous structure, A/B-tested markup).
• A one-shot interaction where writing out a selector chain isn't worth it.
• Prefer browser_click / browser_fill / browser_snapshot directly when you already know the exact selector or ref (@e1) — browser_act adds LLM latency + cost that a direct selector call doesn't.

Steps

1. Call browser_act with a task string, and optionally url (navigates first) and session (default "default"):

   mcp__plugin_ruflo-core_ruflo__browser_act({
     task: "Click the login button",
     url: "https://example.com/account",
     session: "my-session"
   })
   

2. Read the response contract:
   • { success: true, result, steps, history, contentFlagged, llmSource } — the intent executed. result is the AIDefence-gated final text page-agent produced; history is the full step trace (reflection + action + tool result per step); steps is history.length.
   • { success: true, degraded: true, reason, hint } — page-agent isn't installed, or no OpenAI-compatible LLM provider is configured. Never treat degraded: true as an error to retry — surface the hint and fall back to selector-based browser_* tools instead.
   • { success: false, error, ... } — a real failure (browser open failed, injection failed, execution timed out, or page-agent's own execute() reported success:false).
3. On contentFlagged: true, the returned result has already been redacted by AIDefence (PII or a prompt-injection/threat pattern was detected in the page-agent output) — do not attempt to recover the original text.
4. Prefer a recorded session (browser-record) when the interaction matters enough to replay later; browser_act itself does not open an RVF container — it operates on whatever session id you pass (or "default").

Provider requirements (why this degrades so often)

page-agent calls its LLM directly from the browser page context via a plain OpenAI-compatible POST {baseURL}/chat/completions. That means:

• A bare ANTHROPIC_API_KEY is not sufficient — Anthropic's native API is a different shape (/v1/messages).
• Configure one of: OPENROUTER_API_KEY (OpenRouter, OpenAI-compatible), OLLAMA_API_KEY (Ollama Cloud, OpenAI-compatible), or CLAUDE_FLOW_PAGE_AGENT_BASE_URL + CLAUDE_FLOW_PAGE_AGENT_API_KEY for a custom OpenAI-compatible endpoint.
• The real provider key never enters the page: browser_act starts a short-lived loopback HTTP proxy that holds the key server-side and injects the real Authorization header itself. The page only ever sees a 127.0.0.1 URL and a placeholder key string.

Caveats

• page-agent is an optionalDependencies entry (npm i page-agent if the doctor/degraded hint asks for it) — this plugin stays fully operational without it; you simply lose the natural-language layer and fall back to selector-based tools.
• The npm bundle's demo auto-init tail (which would otherwise construct a second PageAgent instance against Alibaba's public test endpoint) is stripped before injection — you should never see traffic to a page-ag-testing-* host from this tool.
• Every successful browser_act call best-effort records the intent + resulting trajectory into the browser memory namespace (ADR-174 distillation loop). This is fire-and-forget — a memory-store failure never fails the tool call.
• timeoutMs (default 120000) bounds how long browser_act polls for execute() to settle; a slow multi-step intent may need a higher value.