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

No annotations are present, so the description carries the behavioral disclosure burden. It discloses key traits: the extension starts from the last second, input requirements, fixed output format/resolution, and cost. It does not cover all behaviors (e.g., async behavior, error handling), but the provided details add substantial transparency beyond the 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 well-structured and concise: a lead sentence with the core purpose, followed by bulleted requirements and cost. Every sentence adds value, no filler or redundancy. It is front-loaded and easy to scan.

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 tool with no output schema and no annotations, the description covers the essential context: input constraints, output format, cost, and continuation behavior. It does not explain the wait parameter's implications (async workflow) or how to retrieve the result, but the schema documents wait and the sibling get_video_status implies the flow. Overall, it is fairly 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?

The input schema already has 100% coverage with descriptive comments for all parameters, so the baseline is 3. The description adds context about input video constraints (duration/fps/resolution) and output resolution, which relates to the video parameter, but it does not provide per-parameter semantics beyond the schema. This is adequate but not exemplary.

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 the specific verb 'extend' with the resource 'existing video' and a precise duration (7 seconds). It clearly distinguishes from sibling tools: generate_video creates new videos, interpolate_frames alters frame rates, and get_video_status checks status. No ambiguity about what this tool does.

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 establishes clear context: use this tool when you have an existing video and need to extend it. It also provides input constraints (duration, fps, resolution) that help the agent decide if the tool is appropriate. However, it does not explicitly name alternatives or state when not to use it, so it falls short of a 5.

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 does so well. It discloses pricing per second, audio always included, model constraints (4K not available for lite), resolution/duration dependencies, the async behavior via the wait parameter, and that generate_audio is ignored on Gemini API. It does not cover error handling or return format, but the provided details exceed the minimum.

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 uses compact bullet points and clear grouping (Supports, Models, Pricing), making it easy to scan. It is longer than average but every section earns its place—especially pricing, which helps select the right model. No filler or redundancy, though it could be trimmed slightly by folding pricing into the model list.

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 tool with 14 parameters, no annotations, and no output schema, the description covers the essential decision points: modes, models, pricing, constraints, and async options. It does not describe the return value or error scenarios, but the schema covers parameter details, and the description compensates for the lack of annotations. A near-complete picture for a complex 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 already covers all 14 parameters with descriptions, so the baseline is 3. The description adds meaningful value beyond the schema: pricing differences inform model selection, constraints like '1080p/4K require duration_seconds: 8' and 'Max 3 asset images or 1 style image' are highlighted, and the note about audio always being generated adds practical semantics that the schema omits.

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 a video using Google Veo 3.1 API.' It clearly distinguishes from sibling tools (extend_video, interpolate_frames, get_video_status) by focusing on creation from text or image. The supported modes are explicitly listed, leaving no ambiguity about the tool's core function.

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 rich context about models, pricing, and supported input types, but it does not explicitly state when to use this tool vs. the sibling tools (e.g., extend_video, interpolate_frames). No exclusionary guidance is given, so the agent must infer from the name and scope that this is for new video generation only.

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. It discloses that the tool returns status (done/pending) and video URL when completed, and that it can download the video. It doesn't mention edge cases like errors or polling behavior, but it is transparent about the main actions and outcomes.

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 sentences, front-loaded with the primary purpose. No filler or redundant information; each sentence earns its place by covering status, return value, and download capability.

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 status-checking tool with no output schema, the description adequately covers the core functionality, return values, and usage guidance. It provides enough context for an agent to select and invoke the tool correctly, especially given the sibling tool references.

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 100%, so the parameters are already well-documented. The description adds minimal new meaning beyond the schema, and the schema already explains that `output_path` implies `download` and that `operation_name` comes from sibling tools. Thus a baseline score of 3 is appropriate.

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 checks the status of a video generation operation, distinguishing it from sibling tools that generate or modify videos. It also specifies the additional capability of downloading the completed video, making the tool's purpose 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?

The description explicitly says to use it after `generate_video` with `wait: false`, providing a concrete usage context. It also names the sibling tools that return the `operation_name`, giving clear guidance on when this tool is appropriate.

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 transparency burden. It adds useful details like pricing and 'audio always included', but it does not disclose async behavior, return formats, or side effects such as saving to output_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 tight and front-loaded: first sentence states the core purpose, second clarifies behavior, and the third adds relevant pricing/audio context. Every sentence earns its place with no redundancy.

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 complex video-generation tool with 7 parameters and no output schema, the description gives adequate purpose and pricing but omits return-value behavior, asynchronous operation details, and explicit usage scenarios. It relies heavily on the schema for parameter-level completeness.

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 100% parameter description coverage, so the schema already documents all parameters. The description adds no parameter-specific meaning beyond mentioning pricing and audio, which are already hinted at in the generate_audio parameter note.

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 action ('Generate a video that smoothly transitions between two keyframes') and names the API (Veo 3.1). It clearly differentiates from sibling tools like generate_video and extend_video by emphasizing the two-frame interpolation behavior.

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 tool is for creating interpolated motion between two keyframes, but it does not explicitly state when to choose it over generate_video or extend_video. The only sibling mention is a pricing note, not usage guidance.

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