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

With no annotations provided, the description must carry the full burden of behavioral disclosure. It does so effectively: states that images are saved to output_path, that parent directories are created automatically, and that PNG/JPEG formats are supported. It also notes model-specific size limitations and the 4096px maximum. It does not address potential costs, rate limits, or authorization requirements, which is a minor gap.

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 front-loaded with a one-sentence summary followed by a well-organized Args section. Every sentence adds functional value: parameter explanations, examples, defaults, and caveats. There is no filler or repetition. The structure makes it easy for an agent to scan and extract key constraints. At ~8 sentences for 6 parameters, it is appropriately sized.

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?

The tool has 6 parameters, an output schema, and no annotations; the description covers all parameter semantics and high-level return behavior ('Success message with output path, or error description'). It references list_models() to aid model selection. Missing is an explicit disqualifier for when to use transform_image instead, but overall this is a thorough description that enables correct invocation in most cases.

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?

Despite the input schema having 0% description coverage, the description documents all 6 parameters in detail: prompt includes a concrete example and guidance; model_id references list_models() and gives examples; output_path explains save behavior and formats; reference_image clarifies its purpose; width and height include defaults, common values, and max constraints. This far exceeds the schema's minimal type/title information.

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 opens with a specific verb+resource: 'Generate an image from a text prompt using the specified model.' This clearly distinguishes the tool from siblings like transform_image (which would modify an existing image) and list_models (which lists available models). The phrase 'generate a new image' in the reference_image parameter reinforces the 'create from scratch' semantics.

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?

The description strongly implies usage through phrases like 'using the specified model' and 'model_id: Model identifier from list_models().' It clarifies that reference images influence generation, not transform existing files. However, it does not explicitly name transform_image as an alternative for editing scenarios, and it lacks an explicit 'Use this tool when...' or 'If you need to edit, use...' statement. Nevertheless, the distinction from siblings is inferable from the action verbs.

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

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

With no annotations, the description carries full responsibility for transparency. It discloses that models are fetched dynamically from each provider's API, indicating a live network operation. However, it does not explicitly state that it is read-only, potential latencies, or failure modes if providers are unavailable. This is adequate but not comprehensive.

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 concise with exactly three sentences, each serving a purpose: stating the core function, describing the output and its use, and noting the dynamic data source. No extraneous content or repetition.

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 no-parameter listing tool, the description covers the essentials: what it lists, the output format (formatted list of IDs), and how data is obtained. Given an output schema exists, it doesn't need to detail the return structure. Minor gaps include potential error behavior and network requirements, but these are not critical for this simple tool.

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 has zero parameters, so the schema coverage is trivially 100% and the description needs to add no parameter details. Baseline for 0 params is 4, and the description confirms no arguments are needed by focusing entirely on the listing operation.

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 states the tool's function with a specific verb ('List') and resource ('available image generation models from all enabled providers'). It distinguishes itself from sibling tools by explicitly noting the output (model IDs) and its relationship to generate_image and transform_image.

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?

The description implies the primary use case: retrieve model IDs before using generate_image or transform_image. It provides clear context but does not explicitly state 'use this before other tools' or mention when not to use it. The linkage to sibling tools effectively guides usage.

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

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

No annotations are provided, so the description carries the burden of disclosure. It states that the tool 'returns guidelines', which is a clear behavioral outcome. However, it does not explicitly mention side effects (likely none) or whether any authentication is needed, leaving some ambiguity for a tool with zero annotation support.

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 extremely concise—two sentences with no redundancy. The first sentence front-loads the purpose, and the second clarifies the return value. Every word contributes meaning.

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?

Given the tool's simplicity (no parameters, no nested objects) and the presence of an output schema, the description covers the essential context. It clearly differentiates from sibling tools and fully explains what the tool delivers. The sibling names provide additional context that aligns with the description.

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 input schema has no parameters, and the schema description coverage is 100% (trivially). Per the baseline for 0 parameters, a score of 4 is appropriate since no parameter semantics are needed and the description doesn't need to compensate.

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 states the tool's function with a specific verb ('Get') and resource ('best practices and examples for writing effective image generation prompts'). It distinguishes itself from siblings like generate_image and transform_image by focusing on guidance rather than action.

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?

The description implies usage for improving image prompt quality but offers no explicit when-to-use or comparison with alternatives. With sibling tools present, more explicit guidance would be helpful, but the context makes the primary use case evident.

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

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

No annotations are provided, so the description carries the burden. It discloses that the output is saved to output_path, that parent directories are created automatically, and that common image formats are supported. It does not explicitly address overwrite behavior or original-file preservation, but the explicit output_path sufficiently implies a new file is written.

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 efficiently structured with a one-sentence purpose, a clear Args list, and a Returns line. Every sentence contributes meaning; there is no fluff or repetition. It is appropriately sized for a 4-parameter tool.

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?

Despite having no annotations and an unrevealed output schema, the description is self-sufficient: it explains what the tool does, every parameter, the expected output, and even auxiliary details like automatic directory creation and model sourcing. This allows an agent to invoke it correctly without external knowledge.

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?

Schema coverage is 0%, and the description compensates fully. Each parameter is explained with semantics: image_path includes format support, prompt includes multiple examples, model_id lists valid examples and references list_models(), output_path clarifies directory creation. This adds far more than the bare schema property names.

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 states the tool's function: 'Transform an existing image based on a text prompt.' This specific verb-resource pair distinguishes it from sibling generate_image (which creates new images). The phrase 'existing image' is an explicit scope marker.

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?

The description gives clear context: it is used to modify existing images with a text prompt. It also references list_models() for model selection, guiding users on how to obtain valid model IDs. However, it does not explicitly name alternatives or state when not to use this tool, only implying the contrast with generation.

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