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Pictomancer.ai

by ai.pictomancer.api

compress_image

Compress an image

Re-encode an image with quality/format options to reduce file size. Supports jpeg, png, webp, tiff, gif. Instead of a q number you can set quality_target (0-1]: the smallest file with SSIM at or above the target, searched on the worker (jpeg, webp, avif; flat surcharge; outcome reported in X-Pictomancer-Quality-* headers). If the output is not smaller than the input, the request is free (X-Pig-Billed: 0) and does not consume free-tier quota. Optional enhancement modifiers: denoise (1-3), equalize, sharpen (applied denoise -> equalize -> op -> sharpen).

Responses:

200: Processed image binary (Success Response) Content-Type: application/json Content-Type: image/jpeg

Example Response:

"string"

Content-Type: image/png

Example Response:

"string"

Content-Type: image/webp

Example Response:

"string"

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
qNoQuality (1-100). Lower = smaller file. Typical values: 60-80 for web, 85-95 for print. Maps to libvips Q parameter.
stripNoStrip metadata (EXIF, ICC profile, etc.) from the output. Reduces file size slightly.
formatNoOutput format: jpeg, png, webp, tiff, gif, or avif. If omitted, the original format is preserved.
sourceYesImage source: a public URL (https://...) or a base64-encoded string (optionally as a data URI like data:image/png;base64,...).
autorotNoApply EXIF orientation before compressing. Opt-in; default false, which preserves current byte-for-byte behavior.
denoiseNoMedian denoise before compressing: radius 1-3 (window 3x3 to 7x7). Opt-in; no surcharge.
sharpenNoUnsharp-mask sharpen after compressing (libvips defaults). Opt-in.
deliveryNo
equalizeNoAuto-contrast (histogram equalisation of the value channel; hue and saturation preserved) before compressing. Opt-in.
quality_targetNoTarget SSIM (0-1]: the worker searches for the smallest file that still scores at least this. Alternative to q; mutually exclusive with it. Requires an explicit format among jpeg, webp, avif. Carries a flat surcharge for the extra encodes.

TDQS

A4.7/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations are provided, so the description carries full burden. It discloses critical behaviors: free-trial condition, search process for quality_target, pipeline order of modifiers, delivery modes with security details, and billing headers. Very comprehensive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Well-structured: starts with purpose, then details on quality_target, free condition, modifiers, then response examples. Every sentence adds value; no redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given 10 parameters, high schema coverage, no output schema but includes response examples and headers (X-Pig-Billed, X-Pictomancer-Quality). Covers input, delivery, modifiers, security, and outcomes completely.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is high (90%), baseline 3. The description adds meaning beyond schema: mutual exclusivity of q and quality_target, surcharge for quality_target, modifier effects, and security notes on delivery parameters.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with 'Compress an image' and explains it re-encodes with quality/format options to reduce file size. It lists supported formats and distinguishes from sibling tools like convert_image by focusing on compression to reduce size.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides explicit guidance on when to use q vs. quality_target, notes that the request is free if output is not smaller, and describes optional modifiers. However, it does not directly contrast with siblings (e.g., when to use convert_image instead).

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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TDQS

A4/5.0
Disambiguation4/5

Most tools have clearly distinct purposes (analyze, compress, convert, crop, resize, pipeline, optimize_for_vision, optimize_generated_image). However, there is some overlap between compress_image and convert_image (both deal with quality settings and can change format), and between resize_image and crop_image (resize's fill mode with smart-crop overlaps crop's smart crop). The pipeline tool could theoretically subsume any of the single-operation tools, which introduces a slight ambiguity in when to use pipeline vs. individual tools.

Naming Consistency5/5

All tool names follow a consistent verb_noun pattern (analyze_image, compress_image, convert_image, crop_image, get_format_info, image_pipeline, optimize_for_vision, optimize_generated_image, resize_image). The naming is predictable and self-documenting, with no mixing of camelCase or other conventions.

Tool Count5/5

With 9 tools, the server is well-scoped for an image processing domain. Each tool covers a core operation (analyze, compress, convert, crop, resize, pipeline, format info, and two optimization tools). The count feels appropriate—not too few to limit usefulness, not too many to be overwhelming.

Completeness5/5

The tool set provides comprehensive coverage for common image manipulation tasks: analysis, compression, format conversion, cropping, resizing, optimization for both general and AI-generated images, and a pipeline for chaining operations. Missing features like rotation, flipping, or color adjustments are minor but the core CRUD-like operations (read/analyze, write/convert, resize/crop) are well-represented, and the pipeline tool mitigates gaps by allowing combinations.

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