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

With no annotations, the description carries the burden and discloses key behavior: polling, optional waiting, downloading the finished video, and returning an asset id/path. It does not cover failure/expiry states, rate limits, or permissions, but the main side effects and outcome are stated.

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 three compact sentences with no wasted wording. The core action and the important omit-parameter behavior are front-loaded.

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 two-parameter polling tool with no output schema, the description covers the return value (asset id/path), the optional wait behavior, and the listing mode. It is nearly complete, though it omits what happens on failed or expired operations.

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 only 50%: waitSeconds is well documented in the schema, while operationName has no schema description. The description adds meaning for operationName by explaining that omitting it lists all pending renders, but it does not define the expected format or source of operationName, leaving a gap.

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 a specific verb and resource: check a pending Veo operation, with the behavior when finished and the listing mode when operationName is omitted. It is clear enough to distinguish from generation siblings by the 'pending operation' framing, but it does not explicitly name or contrast with flow_list_assets or flow_generate_video.

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?

It gives a concrete usage rule: call without operationName to list all pending renders, and optionally wait via waitSeconds. It does not provide explicit when-not conditions or comparisons to sibling tools such as flow_list_assets, but the core usage contexts are clear.

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 formal annotations, the description carries the full burden and excels. It discloses crucial operational behavior, including the 2-day retention limit of remote files and the 720p resolution constraint, which is vital for an agent making quality-sensitive decisions.

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 definition is extremely concise. It front-loads the main function and immediately follows up with essential behavioral constraints (source required, time limit, quality limit) without any unnecessary fluff.

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?

While the core function and the most critical limitations are covered, the lack of semantic guidance on the 3 unspecified parameters ('wait', 'model', 'timeoutSeconds') means the definition is not fully complete for a complex multi-parameter 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 input schema coverage is low at 40%. The description focuses on the overall action and constraints, providing no semantic explanation or context for parameters like 'wait' or 'timeoutSeconds,' forcing the AI agent to rely entirely on schema documentation for the technical parameters.

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 defines the tool's function as continuing a previously generated video asset. By naming it 'Extend,' it distinguishes the action from the 'flow_generate_video' sibling tool. It clearly states the action but could be slightly more explicit about the chain of operation.

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 provides critical usage context: the source video must be generated by this specific server. It also outlines operational constraints regarding file retention and resolution, which dictates the proper use case and expectations for the agent.

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 full disclosure burden. It does useful work: it explains the mode switch on inputImages and that it returns local paths plus a preview. But it never says whether generation is synchronous or returns an operation id to poll (relevant given the flow_check_operation sibling, which implies async work exists), nor anything about cost, latency, or failure modes. A meaningful gap for a generation call with zero annotation coverage.

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?

Four sentences, front-loaded with capability, then mode logic, then workflow handoff, then return shape. Nearly every sentence earns its place; the only soft spot is the 'as in Flow's image tools' clause, which adds little for an agent.

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?

With no output schema, the description correctly compensates by naming the return values (local paths and a preview). It is nearly complete for a creative-generation tool; the missing piece is whether the call blocks until the image is ready or requires polling via flow_check_operation.

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 71%, so the schema documents most parameters, including aspectRatio guidance for video keyframes. The description adds genuine semantics the schema does not: the presence or absence of inputImages is what selects text-to-image versus edit/restyle/combine, which reframes inputImages as a mode flag rather than a plain array. It does not elaborate on model overrides or numberOfImages.

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?

States a specific verb and resource (create/edit images) plus the underlying models (Nano Banana / Imagen), then splits the tool into two clearly named modes: text-to-image without inputImages, and edit/restyle/combine with them. This is enough to distinguish it from flow_generate_video and the other siblings without opening a schema.

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?

Gives concrete usage context: build characters, props and keyframes, then hand the returned asset ids to flow_generate_video as startImage/endImage/referenceImages. That is an explicit workflow handoff. It stops short of stating when NOT to use it (e.g. when to prefer flow_generate_video or flow_extend_video), so it is clear context rather than full routing guidance.

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 the full burden and does so: 1-6 minute render latency, default blocking behavior via wait, the pending/operationName fallback path, and that files are written locally with their paths returned. It also flags that image arguments accept either asset ids or local file paths, which is non-obvious side-effect-adjacent context.

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?

Purpose is front-loaded and every sentence adds information; the prompt-writing and latency sentences both earn their place. It is a single dense block rather than structured sections, so the mode list and the async-fallback instruction take a careful read to separate.

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?

Covers the things an agent cannot infer: render time, blocking vs non-blocking, where output files land, and the pending-operation handoff. For a 16-parameter, no-annotation, no-output-schema tool it omits cost implications and safety-relevant behavior (personGeneration, content restrictions), which the agent must infer from the schema.

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 already 88%, so the 3 baseline applies, but the description adds genuine value by tying startImage/endImage/referenceImages to named modes and by spelling out what a good 'prompt' should contain (subject, action, camera move, lens, lighting, dialogue in quotes, SFX). It leaves several params (numberOfVideos, resolution, aspectRatio, negativePrompt, personGeneration, fast, seed) entirely to the schema.

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?

States a specific verb and resource ('Create a short video clip with Veo') and immediately enumerates the three generation modes, which cleanly separates it from flow_generate_image (stills) and flow_extend_video (extending an existing clip). The scope constraint 'short video clip' plus the duration bounds makes the intent unambiguous.

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?

Explicitly maps each mode to its required inputs ('text-to-video (prompt only)', 'frames-to-video (startImage, optional endImage)', 'ingredients-to-video (referenceImages...)'), states the prompt-unless-startImage condition, and routes the agent to flow_check_operation when status=pending. It even points at flow_list_assets / flow_generate_image as the source of image ids.

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?

Annotations already indicate readOnlyHint=true, so the description adds useful behavior: results are newest first, scoped to assets created by this server, and include ids, prompts, and local paths. It lacks detail on pagination defaults and rate limits, but adds substantial context beyond the annotation.

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 a single front-loaded sentence with no wasted words. It efficiently conveys the resource, scope, ordering, and return fields.

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 list tool with no output schema, the description covers the core return content and sort order, which is helpful. However, with 0% schema description coverage, the missing explanation of 'kind' and 'limit' leaves the definition only minimally complete.

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 description coverage is 0%, so the description must compensate for two undocumented parameters. It does not explain the 'kind' filter (video vs image) or the 'limit' parameter at all, leaving both optional parameters semantically opaque.

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 uses a specific verb ('List') and resource ('videos and images created by this server'), making it clearly distinct from the generation and model-listing siblings. An agent can immediately tell this is the read-only asset inventory tool.

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 listing purpose is implied by the description, but there is no explicit guidance on when to use this versus flow_list_models or the generation tools. No exclusions or alternative-selection advice are provided.

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?

Annotations already declare readOnlyHint=true, and the description adds real context beyond that: results are scoped to the caller's API key and also expose the server's configured defaults. It stops short of noting rate limits or whether the list can change over time, but it meaningfully exceeds the annotation.

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?

A single sentence with no filler, leading with the verb and the resource. Every clause (key scoping, defaults) adds distinct 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?

For a zero-parameter, read-only discovery tool with no output schema, the description conveys enough about what comes back (model ids plus defaults). A small gap remains: it does not say whether the defaults are a separate field or embedded per model entry, which matters slightly with no output schema to consult.

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 takes zero parameters, so there is nothing for the description to disambiguate; the baseline for a parameterless tool applies. No misleading parameter claims are present.

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?

States a specific verb (list) and a precise resource (Veo / image model ids), and adds the scope qualifier 'your API key can use' plus the server-configured defaults. It is unmistakably distinct from the generation/asset siblings, which return media rather than model 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?

Usage is only implied: an agent can infer this is a discovery call to run before generating video or images. There is no explicit 'call this first' directive and no statement of when not to use it versus flow_list_assets, so guidance stays at the implied level.

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