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

With no annotations, the description must carry the full behavioral burden. It usefully discloses cost behavior – 'Deterministic text_card and logo_outro blocks consume 0 AI render credits' – which is real economic context. But it says nothing about whether generation is asynchronous, what the tool returns (job ID? URL?), expected wait time, authentication, or rate limits, all of which matter for a long-running render operation.

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 output mode is front-loaded in the first sentence, followed by the two composition systems, and the credit-cost note is placed last. It is dense but every sentence carries information; the 80-preset enumeration is left to the schema rather than dumped into the description. Slightly heavy in one paragraph but efficient overall.

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 an 18-parameter, no-annotation, no-output-schema generation tool, the description covers inputs and credit economics reasonably, but omits the return value and async behavior entirely. An agent does not know whether this call blocks, returns a video URL, or enqueues a job that must be polled with vadyu_get_video_status. That is a material completeness gap.

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 schema already documents all 18 parameters in detail. The description mentions image_url, image_base64, recipes, blocks and formats, but adds no syntax, precedence, or formatting detail beyond what the schema provides. Baseline 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 opens with a specific verb+resource: 'Generate a commercial AI product video ad from a product photo URL (image_url) or base64 image.' It also names the three input modes (recipes, custom blocks, storyboards). It does not explicitly contrast with siblings like vadyu_list_recipes or vadyu_get_video_status, but the function is distinct enough that an agent can tell it apart.

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 explains that the tool 'supports both' recipes and custom blocks and 'multi-beat storyboards', which implies three usage paths, but it gives no explicit when-to-use guidance (e.g., when to pick a recipe vs. a storyboard vs. custom blocks). The schema says the sequence is 'used when recipe and blocks are omitted', but that routing logic is not surfaced in the description.

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 must carry the full behavioral burden. It does convey that this is a lookup scoped to the authenticated user, but it never states that the call is read-only / side-effect free, whether results are live or cached, or any rate-limit behavior.

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 front-loaded sentence with zero filler; every clause names a distinct returned field. No repetition of the tool name and no padding.

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?

Because there is no output schema, the description usefully enumerates the values returned (plan, remaining credits, total quota, eligibility). For a zero-parameter read tool this is nearly complete, with only the read-only/no-side-effects guarantee left unstated.

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 parameter-level to explain; baseline 4 applies. No parameter claims in the description conflict with the empty 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 ('Check') plus the exact resource: the authenticated user's active subscription plan, remaining monthly render credits, total quota, and generation eligibility. This is unmistakably distinct from every sibling tool, which deal with videos, forks, recipes, and sequences.

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 purpose implies when to call it (before attempting a render to confirm eligibility/credits), but the description never says so explicitly and names no alternatives or preconditions. Adequate but leaves the routing implicit.

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 exist, so the description carries the whole burden; it does add genuinely useful context by naming the returned artifacts, including a 'signed' MP4 URL (implying time-limited access) and a Campaign Bundle. However, it omits key behavioral details for a polling tool: possible status values, what happens while rendering is incomplete, error behavior for unknown IDs, and any rate/polling guidance.

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?

It is a single front-loaded sentence with the action verb first and no filler. The long enumeration of return fields is dense but each item earns its place; it would read better split into an action clause and a returns clause.

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 compensates well by enumerating the return payload (status, timeline, coherence score, video_url, Campaign Bundle), and the inputs are fully covered by the schema. The remaining gap is behavioral edge cases for polling, which is minor for a low-complexity three-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?

Schema description coverage is 100% and all three parameters (video_id, video_ids, export_format with its four enum meanings) are fully documented in the schema itself. The description only restates the ID-addressing modes and adds no syntax, precedence (video_id vs video_ids), or format-selection guidance beyond 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?

The description gives a specific verb ('Poll') plus the exact resource (rendering status, beat progress, coherence score, signed MP4 URL, Campaign Bundle) and states both addressing modes (numeric video_id or batch video_ids[]). This clearly separates it from vadyu_list_videos (enumerate) and vadyu_generate_video_ad (create), so an agent can pick it without opening the 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?

The verb 'Poll' implies the check-until-ready workflow, and the batch wording hints at aggregating matrix renders, but the description never states when to use this versus vadyu_list_videos or vadyu_get_account_quota, nor any prerequisite (e.g. call after vadyu_generate_video_ad). Usage is implied rather than stated.

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 must carry the behavioral burden. It discloses the scope of returned data (80+ recipes, block schemas, moods, credit rules), which is useful context, but it does not clarify pagination, response size, permissions, or whether the operation is purely read-only (though 'List' implies it). No contradictions exist.

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 dense sentence that is front-loaded with the main action. It is information-rich without filler, though the long enumeration of internal block schema names and audio moods may be more detail than needed for tool selection.

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 there is no output schema, the description should fully explain return values; it does enumerate content categories, which helps, but it omits pagination, response format, and how the optional filters and recommendations interact. It is adequate but leaves notable gaps.

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 baseline is 3. The description mentions 'optional smart recipe recommendations' which maps to `recommend_for`, but it adds no syntax, format, or interaction details beyond what the schema already provides for all three 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 uses a clear verb ('List') and names the resource ('Composable Director Recipes'), and it enumerates the bundled data (signature presets, Recipe Foundry catalog, block schemas, audio moods, credit rules). However, it does not differentiate this tool from siblings like `vadyu_list_sequences_and_formats` or `vadyu_list_videos`, leaving the agent to infer scope.

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?

There is no explicit guidance on when to use this tool versus alternatives, no exclusions, and no stated prerequisites. The only conditional hint ('optional smart recipe recommendations') is tied to a parameter, not to tool selection.

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 and no output schema, so the description carries the full burden. It partially meets that by naming the attributes returned ('beat counts, durations, and prompt best practices'), which tells the agent this is reference data rather than generated content. It does not state that the data is static, read-only, or whether any prerequisite/auth applies.

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 front-loaded sentence with no filler; every clause names a returned resource or attribute. Slightly dense enumeration, but nothing is wasted.

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 parameters, no annotations, and no output schema, the description covers what the agent needs: what the tool enumerates and which fields come back. Only the ambiguity around recipes (also covered by vadyu_list_recipes) and the static/read-only nature are left unresolved.

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 the baseline of 4 applies. There is no parameter semantics to explain and the description correctly does not invent any.

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 names a specific verb ('List') and enumerates exactly what is returned: sequences/storyboards, Composable Director Recipes, audio moods, and aspect ratio formats. That is clear, but the overlap with the sibling vadyu_list_recipes (recipes are listed here too) is never addressed, so the agent cannot tell from the description alone which tool to open for recipes.

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?

No explicit when-to-use or when-not-to-use statement, nor any pointer to alternatives. Usage is only implied: an agent can infer this is a discovery call to run before vadyu_generate_video_ad, but nothing in the text says so.

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 burden; 'List' reasonably implies a read-only operation and it discloses the available status filter values and that results are paginated. It does not state ordering, default page size behavior, what a returned item contains, or any rate/permission constraints beyond account scoping.

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 front-loaded sentence with no wasted preamble; the account scope comes first and the optional filter/pagination follow. The parenthetical status list is slightly redundant but does not bloat the text.

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 low-complexity, zero-required-parameter list tool with full schema coverage and no output schema, the description covers what the agent needs to invoke it correctly. Minor gaps remain around result ordering and typical response contents, but neither blocks a correct call.

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 page, limit, and status are already fully documented with defaults, bounds, and an enum. The description's parenthetical enumeration of 'done', 'processing', 'failed' merely repeats the schema enum and adds no new constraint or format detail.

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 (list generated video ads) plus scope (authenticated Vadyu account), which cleanly separates it from siblings like vadyu_list_recipes, vadyu_list_studio_forks, and the single-item vadyu_get_video_status. It stops short of naming an alternative explicitly, so it is clear but not maximally differentiated.

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 implied: call this to enumerate video ads, optionally narrowed by status. It gives no explicit when-to-use vs when-not or routing to alternatives such as using vadyu_get_video_status for a single video, leaving the agent to infer that.

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 must carry the full burden. It does disclose that the output goes to a human team at Vadyu (non-executing, review-oriented), which is useful context beyond the schema, but says nothing about confirmation behavior, review turnaround, duplicate handling, or any constraints on submission volume.

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 front-loaded sentence that names the action, the accepted content types, and the recipient — no filler or repetition. It could be marginally tightened but earns its length.

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 is simple (5 flat params, full schema coverage, 1 required), so not much is needed. However, the description omits what happens after submission (acknowledgement, review, rejection) and does not clarify that only 'storyline' is required while title/audio_mood/brand text are optional.

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 every parameter is already documented with examples. The description's mention of 'storyline blueprint' and 'recipe idea' loosely maps to the storyline/title params but adds no syntax, format, or optionality guidance beyond the schema. Baseline 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?

States a specific verb ('submit') and enumerates the three resources accepted (custom ad recipe idea, multi-scene storyline blueprint, product feedback) plus the destination (Vadyu's creative engineering team). This clearly separates it from siblings like vadyu_generate_video_ad or vadyu_list_recipes, though it never names those siblings explicitly.

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 phrasing implies 'use this to send ideas/feedback rather than to generate anything', but there is no explicit when-to-use, when-not-to-use, or named alternative. An agent must infer that this is the intake channel versus the execution tools in the sibling set.

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