Compare two images pixel-by-pixel and identify visual differences. Returns structured diff data including: - Overall diff percentage and pixel counts - Clusters of different regions with bounding box coordinates (x, y, width, height) - Severity rating per cluster (trivial/minor/moderate/major) - ...
AI agents call get_diff_of_images to retrieve information from Pointsyeah without modifying anything — typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
This tool retrieves and analyzes information from two images to produce a comparative report. It does not create, modify, delete, execute code, or commit financial transactions. The output (diff percentages, clusters, heatmaps) is informational only. Image comparison is a read operation on image data.
From the tool's definition Tool performs pixel-by-pixel comparison and returns structured diff data, heatmaps, and metadata. No modification, deletion, execution, or financial operations occur.
Attacks that exploit this kind of access
Compare two images pixel-by-pixel and identify visual differences. Returns structured diff data including: - Overall diff percentage and pixel counts - Clusters of different regions with bounding box coordinates (x, y, width, height) - Severity rating per cluster (trivial/minor/moderate/major) - Clustering metadata with the gap used and suggestions for tuning - File paths to generated heatmap images showing diff intensity Clustering: By default, nearby diff regions are automatically grouped using a natural-breaks algorithm that finds the optimal merge distance. This produces a manageable number of clusters where each represents a distinct problem area. To override, set cluster_gap explicitly (0 = no merging, or a specific pixel distance). Heatmap output: A PNG image where diff regions are colored from yellow (subtle difference) to red (major difference). A composite version overlays this on the source image for easy comparison. Use cases: - Compare a design mock screenshot against actual UI implementation - Compare a Figma mock of a single component against a full-page screenshot (auto-aligned) - Detect visual regressions between two versions of a page - Verify CSS/layout changes only affect intended areas Requirements: - Images must be accessible as local file paths - Images can have different dimensions — the smaller image is automatically aligned within the larger one using template matching Note: Anti-aliased pixels (font smoothing, rounded corners) are excluded by default to reduce false positives. Set include_aa=true to include them. It is categorised as a Read tool in the Pointsyeah MCP Server, which means it retrieves data without modifying state.
Register the Pointsyeah MCP server in PolicyLayer and add a rule for get_diff_of_images: 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 Pointsyeah. Nothing to install.
get_diff_of_images 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 get_diff_of_images 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 get_diff_of_images. 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.
get_diff_of_images is provided by the Pointsyeah MCP server (slack-workspace-mcp-server). 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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