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

No annotations exist, so the description carries the full behavioral burden. It discloses accepted input channels and the intended-use gate, but says nothing about return format, latency/cost, whether image data is sent to a remote service, or any permission requirements.

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?

Three tight sentences, front-loaded with the capability, then the usage gate, then example use cases. No filler and nothing buried.

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 two-parameter tool this covers inputs and when-to-use well, but with no output schema and no annotations the description should not be silent on what comes back from the analysis or on the handling of the supplied image data.

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 both parameters are already documented and 3 is the baseline. The description restates the source formats already in the schema and adds no guidance on the prompt parameter (e.g., how specific it should be).

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 ("Analyze an image") and enumerates the accepted source forms (local path, HTTP(S) URL, base64 data URI). It is trivially separable from its only sibling, analyze_video, by resource type, though it never explicitly contrasts with it.

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 an explicit gating rule: use ONLY when you cannot view the image yourself, and answer from native vision otherwise. It also names concrete contexts (screenshots, UI, OCR, diagrams, photos, visual debugging), leaving no inference needed about when to reach for it.

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 burden of behavioral disclosure. It transparently reveals the keyframe-extraction mechanism and source types (local/remote). It does not mention output format or potential limitations (e.g., loss of temporal context between frames), which prevents a top score, but the core behavior is well outlined.

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 exceptionally concise: two sentences, no fluff, with the primary purpose front-loaded and supporting usage guidance in the second sentence. Every word earns its place.

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 no output schema, so the description should clarify the return value. While the description explains the mechanism (extracting keyframes) and gives usage examples, it does not state what the analysis produces (e.g., text summary, labeled frames, etc.), leaving a notable gap for an AI agent selecting and invoking the 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 provides detailed descriptions for all three parameters (source, prompt, max_frames), achieving 100% schema coverage. The description adds no extra parameter-level meaning beyond what the schema already states, so the baseline score of 3 applies.

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: 'Analyze a local or remote video by extracting evenly spaced keyframes.' This specific verb+resource+method distinguishes it from typical image tools. It also lists concrete use cases like 'screen recordings, UI flows, demos, and event summaries,' further clarifying its video-centric purpose.

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 phrase 'Use this for screen recordings, UI flows, demos, and event summaries' offers clear, context-rich guidance on when to use the tool. However, it does not explicitly mention when not to use it or compare it with the sibling tool 'analyze_image,' so it stops short of full alternative 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?

Annotations already declare readOnlyHint=false, openWorldHint=true, idempotentHint=false and destructiveHint=false. The description adds real value beyond that: it discloses the return shape (image content plus metadata) and the optional side effect of writing a new local file, which the annotations alone don't convey. It stops short of mentioning cost, rate limits, or provider auth requirements, which matters for a non-idempotent, open-world generation call.

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?

Three sentences, front-loaded with the core verb and resource, then the provider scope, then the return behavior and the disambiguation. Almost every clause earns its place, with only minor redundancy between 'Returns image content' and the later clarification.

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 fills the gap by describing the return (image content and metadata) and the optional file write. All parameters are schema-documented and safety traits are annotated. The remaining gap is operational context, such as which provider settings are read from configuration and whether generation costs or rate limits apply.

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 all four parameters (prompt, size, quality, output_path) are documented in the schema, including provider-specific caveats like quality being unsupported by Ark. The description only echoes the optional file-saving behavior for output_path and adds no syntax, defaults, or provider-selection logic beyond the schema. Baseline 3 is appropriate when the schema does the heavy lifting.

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 (Generate) and resource (one image from a text prompt), names the supported providers, and explicitly separates itself from the analyze_image sibling with 'This is image creation, not image analysis.' An agent can distinguish it from siblings without reading any 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?

Clearly establishes the context: image generation from prompts, provider-agnostic, and independent of native vision capability, with an explicit statement that it is not image analysis. It does not explicitly say when NOT to use it (e.g., editing an existing image) or name analyze_image directly as the alternative, which leaves a small inference gap.

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