Get supported formats and options
Returns supported output formats and their configurable options.
200: Successful Response (Success Response) Content-Type: application/json
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries the burden. It says the tool 'Returns supported output formats and their configurable options' and includes a 200 response, which is basic behavioral disclosure for a read-only query. However, it does not explicitly state that this operation has no side effects or that it requires no input, though 'Get' implies read-only.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is short and front-loaded with the key purpose. The first sentence slightly restates the tool name, but the second sentence adds concrete value about what the response contains. The 'Responses' section is minimal and provides a small amount of useful context.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
For a zero-parameter, read-only info tool, the description is sufficient: it states what the tool returns and includes a response note. However, it could be more helpful by relating the output to sibling tools (e.g., 'use these formats with convert_image'), but this is not essential.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
There are zero parameters, so the description cannot add parameter-level detail. The baseline for zero-param tools is 4, and the description appropriately clarifies that the tool returns available formats and options instead of performing a transformation.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description states 'Get supported formats and options' with a clear verb and resource, and clarifies it returns supported output formats and configurable options. This distinguishes it from sibling image-processing tools, which perform actions rather than querying metadata.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
There is no explicit guidance on when to use this tool versus alternatives. It does not mention that it should be called before convert_image, compress_image, or other tools to discover supported formats, nor does it mention any exclusions or prerequisites.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Add one secure layer between your agents and this server.
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.
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.
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.
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.