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?
With no annotations, the description carries the transparency burden. It clearly states that the tool returns supported formats and options, which implies a read-only query operation. The absence of side effects or side-channel behavior is adequately conveyed for a tool of this simplicity.
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 but somewhat redundant: 'Get supported formats and options' and 'Returns supported output formats and their configurable options' restate the same idea. The Responses section adds standard HTTP status detail, but the first two sentences could be merged into one efficient line.
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 simple, parameterless query tool, the description is reasonably complete. It states the purpose and the return value. Although there is no output schema, the description explains what is returned (supported formats and options). The sibling context clarifies that this is a non-action informational tool, making the overall picture adequate.
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?
The tool accepts zero parameters, so the input schema provides no semantic content. The description correctly adds no parameter information because there is none to add. Per the rubric, a 0-parameter tool receives a baseline of 4.
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 clearly identifies the tool's function: it gets supported formats and returns their configurable options. The verb 'Get' and the specific resource 'supported formats and options' make the purpose unambiguous, and it is distinct from sibling tools that perform image transformations.
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?
Usage context is implied: given the sibling tools all perform image operations, this is evidently a query/utility tool for retrieving format information. However, there is no explicit guidance on when to use it instead of alternatives, nor any mention of prerequisites or exclusions.
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 are distinct, but compress_image, convert_image, and optimize_generated_image overlap heavily: all re-encode images with format and quality options. The descriptions clarify intent, yet an agent could still struggle to pick between compressing, converting, and optimizing in some cases.
The set mostly follows a verb_noun pattern (analyze_image, compress_image, crop_image, resize_image). Minor deviations include image_pipeline, which is a noun phrase, and optimize_for_vision/optimize_generated_image, which use longer prepositional forms, but the overall pattern is predictable.
Ten tools is well within the ideal range for an image-processing server. Each tool serves a meaningful purpose, and the count feels neither bloated nor thin.
Core image operations are well covered: analyze, resize, crop, convert, compress, pipeline, plus cost/format introspection. Minor gaps like rotate/flip or explicit metadata editing are absent, but agents can accomplish most workflows with the existing surface.