Detect cropped content in device-mockup frames (phone/MacBook screenshots, app-preview clips). Three checks: (1) GEOMETRY — call with frames (container box + intrinsic media size + object-fit/position; call with NO args for a DevTools snippet) to flag object-fit:cover crop loss when the frame's a...
AI agents call audit_device_frame to retrieve information from Raven without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
clips | array | — | Per-clip first/last frame PNG file paths — detects baked-in pan/zoom (Ken Burns). |
frames | array | — | Device-frame geometry samples (from the DevTools snippet): container box + intrinsic media size + computed object-fit/position. Flags object-fit:cover crop loss |
edge_frames | array | — | Frame PNG file paths to check for content truncated at a frame edge (reuses edge-symmetry). |
Parameters from the server's own tool schema.
This is a diagnostic/analysis tool that inspects design assets and device frames to identify issues. It takes input parameters (frames, clips, edge_frames as PNG paths) and returns findings about content composition and cropping. No data is modified, deleted, or executed—only analyzed and reported.
From the tool's definition Tool performs detection and auditing operations: 'Detect cropped content', 'flag object-fit:cover crop loss', 'detect baked-in pan/zoom', 'flag content truncated'.
Risk signalsHigh parameter count (19 properties)
Attacks that exploit this kind of access
Detect cropped content in device-mockup frames (phone/MacBook screenshots, app-preview clips). Three checks: (1) GEOMETRY — call with frames (container box + intrinsic media size + object-fit/position; call with NO args for a DevTools snippet) to flag object-fit:cover crop loss when the frame's aspect ratio ≠ the media's; (2) MOTION — pass clips (first/last frame PNG paths) to detect baked-in pan/zoom (Ken Burns) that drifts the composition; (3) EDGE — pass edge_frames (PNG paths) to flag content truncated at a frame edge. Catches the exact failure where a 16:9 clip in a 1.82-AR screen cutout silently slices the bottom, or a Ken-Burns-zoomed source crops content. It is categorised as a Read tool in the Raven MCP Server, which means it retrieves data without modifying state.
audit_device_frame accepts 3 parameters: clips, frames, edge_frames. The full parameter table on this page comes from the server's own tool schema.
Register the Raven MCP server in PolicyLayer and add a rule for audit_device_frame: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Raven. Nothing to install.
audit_device_frame is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the audit_device_frame rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for audit_device_frame. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
audit_device_frame is provided by the Raven MCP server (raven-mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
Every MCP server has a record like this.
Type a name, get the same breakdown: verified identity, auth posture, risk grade, capabilities, recommended policy.
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