Surfagent Perception

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Agent vision for web pages — scene summaries, attention-ranked elements, annotated screenshots, and state diffing via SurfAgent's perception engine.

Install

openclaw skills install surfagent-perception

SurfAgent Perception — Agent Vision Skill

How to see, understand, and verify web pages through SurfAgent's perception engine.

What This Is

SurfAgent Perception gives you human-like page understanding in ~200 tokens instead of parsing a 50K-token DOM. Three MCP tools, one workflow loop.

Without perception: You get a raw DOM dump or a dumb screenshot. You have to figure out what's on the page yourself.

With perception: You get a scene summary, ranked interactive elements, spatial clusters, viewport state, and optionally an annotated screenshot with numbered bounding boxes + a legend mapping each number to a ref.

Requires: SurfAgent daemon running (port 7201) with a managed Chrome instance (port 9222).

The Three Tools

`surf_perceive` — Your Primary Eyes

The main tool. Call this to understand what's on screen.

Parameters:

Param	Type	Default	Description
`tabId`	string	active tab	Target a specific tab
`since`	string	—	State token from a previous call. Includes delta of what changed
`maxAnnotations`	number	15	How many elements to rank (1-50)
`annotate`	boolean	false	Include annotated screenshot with numbered bounding boxes

Returns:

Scene summary — one-liner + top 5 ranked actions + state notes (blockers, modals, forms, auth, scroll %)
Viewport info — scroll position, fold split, document dimensions
Top elements — attention-ranked, with refs for clicking/typing
Clusters — semantic groups of related elements (nav cluster, form cluster, etc.)
State token — pass as since next time to get delta
Annotated screenshot + legend (if annotate: true)

When to use: Start of any page interaction. After navigation. When you need to understand the page before acting.

`surf_annotate` — Quick Visual Reference

Lighter than surf_perceive. Just the annotated screenshot + legend, no scene analysis.

Parameters:

Param	Type	Default	Description
`tabId`	string	active tab	Target a specific tab
`maxAnnotations`	number	15	How many elements to annotate (1-50)

Returns:

Annotated screenshot (base64 PNG) with numbered colored bounding boxes
Legend mapping each number to element ref, role, location, and status

When to use: When you already know the page context but need to identify specific elements visually. Good for "which button do I click?" scenarios.

`surf_scene_diff` — What Changed?

Compare current state to a previous state token. Answers: "did my action work?"

Parameters:

Param	Type	Required	Description
`since`	string	✅	State token from a previous `surf_perceive` call
`tabId`	string	—	Target a specific tab

Returns:

Current one-liner scene description
Delta: new elements, removed elements, value changes, page type changes, auth state changes
New state token for the next diff

When to use: After clicking, typing, submitting, or scrolling. Verifies your action had the intended effect.

The Core Loop

This is the pattern for any page interaction:

1. PERCEIVE  →  Understand what's on screen
2. ACT       →  Click, type, scroll, fill
3. DIFF      →  Verify the action worked
4. REPEAT    →  Back to perceive if more actions needed

Example: Login Flow

Step 1: surf_perceive()
  → "[GitHub · login · logged_out] Login form on GitHub"
  → Top actions: [1] Username input, [2] Password input, [3] Sign in button
  → State token: st_abc_1

Step 2: Type username into element [1], type password into [2], click [3]

Step 3: surf_scene_diff(since: "st_abc_1")
  → "auth state changed, page type changed"
  → "[GitHub · dashboard · logged_in] Dashboard — 4 sections"
  → ✅ Login worked

Step 4: surf_perceive() to explore the dashboard

Example: Verify a Click

Step 1: surf_perceive() → get state token
Step 2: Click the "Submit" button
Step 3: surf_scene_diff(since: token)
  → "1 element removed, modal appeared"
  → "📋 Modal: Order Confirmed (2 actions)"
  → ✅ Submission worked

Example: Monitor a Value

Step 1: surf_perceive() → note BTC price, get token
Step 2: Wait 30 seconds
Step 3: surf_scene_diff(since: token)
  → "2 elements changed: price_display $65,100→$65,234"

Reading the Scene Summary

The scene summary has a consistent format:

[Domain · pageType · authState] Context description

Top actions:
1. Buy Button (button, center-right)
2. Price Input (textbox, top-center)
3. Symbol Search (textbox, top-left)

State notes:
⚠ Cookie banner: Accept cookies to continue (auto-dismissable)
📝 Form: 2/5 fields, submit: ref_submit_btn
📜 Scrolled 45%
🔒 Not logged in

Δ 2 elements changed: price $65,100→$65,234, volume +12.3K

One-Liner Format

[Domain · pageType · authState] Description

Page types: login, signup, feed, detail, dashboard, chat, search_results, checkout, compose, settings, profile, docs, table, media, error_page, captcha, blank, other

Auth states: logged_in, logged_out, session_expired, unknown

State Notes (what to look for)

Symbol	Meaning
⚠	Blocker detected (cookie banner, captcha, auth wall). Check if `auto-dismissable`
📋	Modal is open — has title and action count
📝	Active form — shows filled/total fields and submit ref
🔒	Not logged in or session expired
📜	Page is scrolled — shows percentage

Delta Format

When you pass a since token, the delta section tells you exactly what changed:

URL/title/page type/auth state changes
Elements changed (with from→to values)
New elements appeared (with role)
Elements removed
Regions updated

Annotated Screenshots

When you call surf_perceive(annotate: true) or surf_annotate(), you get:

A screenshot with numbered colored bounding boxes on interactive elements
A legend mapping each number to structured info:

[1] Sign In (button, center) — clickable
[2] Email: user@email.com (textbox, top-center) — editable
[3] Remember me (checkbox, center-left) — unchecked

Color Coding

🟢 Green: buttons, submit actions
🔵 Blue: text inputs, textareas, selects
🟣 Purple: links, navigation
🟡 Yellow: checkboxes, radio buttons, toggles
⚪ Gray: headings, labels, static content

Using Annotations for Action

The legend gives you element refs. Use those refs with SurfAgent's click/type/fill tools to interact with the exact elements you identified visually.

Element Ranking: How Attention Scoring Works

Not all elements are equal. The perception engine scores elements across 7 dimensions:

Centrality — Distance from viewport center (center elements rank higher)
Context relevance — Page-type-aware boosting (login → password/submit boosted, feed → content/compose boosted, dashboard → metrics/actions boosted, checkout → payment/order boosted)
Role score — Buttons > inputs > links > headings > images
Visual weight — Size, font weight, contrast against background
Proximity to focus — Elements near the viewport center or active form
Visibility — Above-fold elements rank higher than below-fold
Action density — Elements in clusters of interactive elements get boosted

Final score = weighted combination → top N returned.

Clusters

Elements are spatially grouped into clusters (50px proximity threshold):

Cluster: "Navigation" (top-left)
  - Home link, Dashboard link, Settings link

Cluster: "Login Form" (center)
  - Email input, Password input, Sign In button, Forgot Password link

Use clusters to understand the page layout at a glance. Each cluster has:

Label (auto-inferred from content)
Bounding rect
Primary action (the highest-scored interactive element in the cluster)
Member elements

Performance

~200 tokens per scene summary (vs ~50K for raw DOM)
<200ms per perceive call (no LLM involved — all heuristic)
State tokens are lightweight ring buffers (5 per tab, 20 tabs max)
Annotated screenshots add ~100-200ms for Canvas overlay

Anti-Patterns

❌ Don't call surf_perceive AND surf_page_state on the same page — perceive already includes everything page_state gives you, plus attention ranking and scene summary. It's redundant.

❌ Don't call surf_annotate unless you actually need the screenshot — the image is large (base64 PNG). If you just need to know what's on the page, use surf_perceive without annotate: true.

❌ Don't ignore state tokens — always capture them. They're your "save point" for diffing later.

❌ Don't perceive after every micro-action — if you're typing into a field, you don't need to perceive after each keystroke. Perceive before the interaction, act, then diff after.

❌ Don't assume element refs are permanent — refs are content-hashed and stable across re-rankings of the same page, but they change when the page content changes. Re-perceive after navigation.

Decision Tree: Which Tool Do I Use?

Need to understand the page?           → surf_perceive()
Need to verify an action worked?       → surf_scene_diff(since: your_token)
Need to visually identify an element?  → surf_annotate()
Need both understanding AND visual?    → surf_perceive(annotate: true)
Just need basic page info?             → surf_page_state() (lighter, no scoring)

Integration with Other SurfAgent Tools

After perceive...	Use this to act
Identified a button to click	`/browser/click` with element ref or coordinates
Found a form to fill	`/browser/fill` with selector from element data
Detected a blocker	`/browser/resolve-blocker` to auto-dismiss
Need to scroll for more content	`/browser/scroll` then `surf_scene_diff`
Want to navigate somewhere	`/browser/navigate` then `surf_perceive`
Detected a captcha	`/browser/captcha/solve`

Daemon HTTP Endpoints (Direct API)

If calling the daemon directly instead of through MCP:

`POST /browser/perceive`

{
  "tabId": "optional",
  "since": "optional state token",
  "maxAnnotations": 15,
  "annotate": false
}

Returns: { ok, scene, viewport, topElements, clusters, stateToken, annotatedScreenshot?, legend? }

`POST /browser/annotate`

{
  "tabId": "optional",
  "maxAnnotations": 15
}

Returns: { ok, annotatedScreenshot, legend }

Both endpoints require Bearer auth token (~/.surfagent/daemon-token.txt).

Surfagent Perception

Install

SurfAgent Perception — Agent Vision Skill

What This Is

The Three Tools

surf_perceive — Your Primary Eyes

surf_annotate — Quick Visual Reference

surf_scene_diff — What Changed?

The Core Loop

Example: Login Flow

Example: Verify a Click

Example: Monitor a Value

Reading the Scene Summary

One-Liner Format

State Notes (what to look for)

Delta Format

Annotated Screenshots

Color Coding

Using Annotations for Action

Element Ranking: How Attention Scoring Works

Clusters

Performance

Anti-Patterns

Decision Tree: Which Tool Do I Use?

Integration with Other SurfAgent Tools

Daemon HTTP Endpoints (Direct API)

POST /browser/perceive

POST /browser/annotate

`surf_perceive` — Your Primary Eyes

`surf_annotate` — Quick Visual Reference

`surf_scene_diff` — What Changed?

`POST /browser/perceive`

`POST /browser/annotate`