Loading ToolBench
17 tools with mostly complete schemas and descriptions, but significant gaps in parameter documentation and error handling guidance. Tool names follow verb_noun convention well (analyze_image_content, remove_background, batch_process). Descriptions average ~120 chars, meeting baseline minimums but lacking LLM-optimized context on WHEN to use each tool vs alternatives. Most parameters have type definitions and descriptions, but many lack constraints (min/max for numeric, enum validation for strings). Output schemas are not documented, LLMs cannot predict return structure. Error handling is minimal; no recovery guidance or actionable error messages visible in code. Security concerns: batch_process and process_directory accept file paths without visible sanitization against path traversal.
Analyze image content and properties to help generate accessible alt text descriptions. Returns technical details and dominant colors.
Convert multiple images to a different format with consistent settings
Apply the same operation to multiple images with configurable concurrency and error handling
Check WCAG color contrast ratio between foreground and background colors. Returns compliance status for AA and AAA levels.
Create an animated GIF from multiple images. Supports custom frame delays and loop settings.
Extract individual frames from an animated GIF. Returns frames as separate images with timing information.
Output schemas not documented. LLMs cannot predict return structure (fields, types, pagination). Tools like batch_process and process_directory return arrays but no per-item success/failure structure is visible.
Path traversal vulnerability: process_directory and batch_process accept 'directory' and 'pattern' parameters without visible sanitization. LLMs could be tricked into accessing /etc/passwd or other sensitive paths.
Error handling lacks recovery guidance. No visible error messages that tell LLMs what to do next (retry, ask user, or fail). Errors should be categorized as retryable vs user-fixable.
| Scored | Grade | Overall | Spec posture | Rubric |
|---|---|---|---|---|
| 2026-09-23 | D | 57 | 2026-07-28+ | v2 |
Generate a complete accessible color palette with WCAG-compliant color combinations.
Generate an image using AI with automatic provider selection or explicit provider choice
Convert an animated GIF to WebP format for better compression and quality.
List all available AI image generation providers and their capabilities
Optimize an animated GIF to reduce file size while maintaining animation quality.
Process all images in a directory with a specified operation
Remove background from an image, returning a transparent PNG
Remove background and replace with color or image
Resize an animated GIF while preserving animation. All frames are resized proportionally.
Simulate how an image appears to people with different types of color blindness (deuteranopia, protanopia, tritanopia).
Suggest WCAG-compliant color alternatives for a given base color. Returns lightened/darkened variations that meet accessibility standards.
Numeric parameters lack min/max constraints. 'timeout' defaults to 30000ms but no bounds visible; 'quality' in batch_process is 1-100 but not enforced in schema. LLMs may pass invalid values.
Descriptions lack WHEN-to-use context. 'Analyze image content' does not explain when to call it vs remove_background or generate_image. LLMs waste reasoning cycles deciding between similar tools.