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

With no annotations provided, the description carries the full behavioral burden and succeeds impressively. It discloses masking is 'prompt-guided rather than pixel-exact' with edge adjustment, size rounding to multiples of 16 with aspect-ratio and 3840 limits, background/output-format coupling ('transparent... requires output_format png or webp'), quality latency tradeoffs ('high... can take over a minute'), and that the full-resolution file is already on disk to be referenced by the returned path.

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 long, but the tool's complexity (8 params, masking nuance, size constraints, format coupling) justifies the length. It is well-sectioned with labeled blocks (MASKING, SIZE, BACKGROUND, QUALITY, RETURNS) and front-loads the core purpose and use cases before parameter details. Minor redundancy exists with schema fields like the non-overwrite filename behavior, so it is not zero-waste, but it is efficiently organized for its scope.

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 no annotations and no output schema, this description covers everything an agent needs to invoke correctly: return format (absolute path, final dimensions, model, quality, .json sidecar, JPEG preview), size behavior verification ('check it if the exact pixel size matters'), mask semantics, format limits, and path resolution. For an 8-parameter generative tool, the completeness is exceptional.

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 100%, so baseline is 3. The description adds real value beyond the schema: exact preset dimensions ('square' 1024x1024, 'hero' 1920x1088), rounding/constraint rules for explicit sizes, the mask being 'the same size as the first reference,' and the quality speed implications. This meaningfully deepens the agent's understanding without repeating schema boilerplate.

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 and resource: 'Create a new image that is guided by one or more existing images (1-8 reference files) plus a text prompt, save it... and return a preview.' It clearly distinguishes itself from the sibling generate_image by centering on reference-guided generation and explicitly framing style-consistency and image-modification use cases.

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 explicit when-to-use guidance: 'use this tool whenever the result must stay consistent with images you already have,' including style batching, modification, and combining images. It also names the exclusion condition and alternative: 'For a brand-new image with no reference, use generate_image.' This is model-level routing with nothing left to inference.

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 provided, the description carries the full burden and meets it impressively. It discloses concrete side effects: files land in the generated-assets directory, a .json sidecar is written, and 'a taken name gets -2, -3, ...' (never overwrites). It reveals timing behavior ('high' can take over a minute), size-correctness caveats (rounded to multiples of 16, aspect-ratio bounds, result text reports actual size), and transparency/format coupling. It also states the return contract in detail, compensating for the missing output schema.

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 long but every sentence earns its place; there is no filler. It is front-loaded with purpose and usage routing, then uses labeled SHOUTED sections (SIZE, BACKGROUND, QUALITY, RETURNS) that make dense parameter logic scannable. The closing note about referencing the saved path tells the agent what to do with the result, closing an otherwise easy-to-miss loop.

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 7-parameter tool with no annotations and no output schema, the description is complete. It covers the operation, side effects (file writes, sidecar), parameter semantics with constraints, timing expectations, and a detailed return contract (absolute path, dimensions, model, quality, preview). Nothing an agent needs to invoke it correctly and interpret results is missing.

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?

Although schema coverage is 100%, the description adds substantial meaning beyond the schema: exact pixel dimensions for every size preset, rounding-to-16 rules, the 1:3–3:1 aspect-ratio bound, and the 3840-pixel maximum are absent from the schema. It adds the transparent-requires-png/webp constraint, explains quality trade-offs (low = fast/cheap for drafts, high = slow over a minute), and the filename never-overwrite behavior. This goes well beyond the baseline 3 justified by full schema coverage.

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 and resource: 'Generate one or more brand-new images from a text prompt... save them as files... return a small preview.' It explicitly contrasts with edit_image ('if you need the new image to match the look of images you already have'), so an agent can distinguish create-from-prompt from edit-from-reference without opening schemas. The phrase 'brand-new' signals no input image is required, which is the core differentiator.

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?

Provides an explicit when/when-not rule: 'Use this when no existing image needs to guide the result. If you need the new image to match the look of images you already have... use edit_image instead and pass those files as references.' This names the sibling alternative and the exact condition that selects it — nothing is left to inference. The prompt-writing guidance (subject, style, composition, lighting, colour palette, text) is also actionable.

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 responsibility for behavioral disclosure. It transparently explains that only images written by the server are listed, that the prompt is included only when a .json sidecar exists, and that the default directory is ./assets/generated. This gives the agent a good understanding of what to expect, though it does not describe edge cases like an empty directory.

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 two sentences with no filler. The first sentence front-loads the core listing behavior and return fields, while the second adds practical session-start guidance. Every clause contributes necessary information.

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 low complexity, the presence of an output schema, and its sibling tools, the description is complete. It covers what is listed, where from, the conditional behavior, and how to use the results, so an agent has everything needed to invoke it correctly.

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 and 100% schema coverage, so there is no parameter ambiguity. The description adds context by confirming there are no options and that calling it simply lists all generated assets, which is sufficient.

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 a specific action and resource: listing images written to the generated-assets directory. It also specifies exactly what data is returned (absolute path, dimensions, size, prompt) and frames the purpose as discovering reusable assets, which separates it from the sibling generation and editing tools.

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 explicit guidance: use it at the start of a session to discover previously created assets and to pass them as references to edit_image. It does not explicitly state when not to use it versus generate_image, but the intended workflow is clear enough to select this tool appropriately.

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