Kulture

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

Annotations already flag destructiveHint and openWorldHint, and the description adds the genuinely valuable context: pro-rated immediate charge, fallback checkout link, and owner/admin authorization. One thing it does not reconcile is idempotentHint=true against 'charged immediately' — an agent could reasonably wonder whether a retry double-charges, and the description never addresses repeat invocations.

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: action first, then the two payment paths, then the permission boundary. Zero filler and every sentence carries decision-relevant 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?

With no output schema, the description still pre-announces the conditional return (secure checkout link) so the agent knows what to do next. It's a destructive, real-money tool with only three params, and the main missing piece is failure/reversal behavior and what happens on a downgrade — minor given the otherwise solid coverage.

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 33%: only confirm_payment is self-documented, and the description doesn't touch it (though the const-true and its description cover it well). Plan and interval are enums so values are unambiguous, but the description adds nothing about what the tiers mean, pricing, or how upgrading/downgrading is handled — no compensation for the low 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 names a concrete verb and resource ('Subscribe the workspace to a Kulture plan') and clarifies the switch case, so an agent can tell it apart from the project/video siblings at a glance. The title's '(real payment)' framing is reinforced in prose by the immediate-charge and checkout-link 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?

It gives the two execution paths explicitly: card on file -> immediate pro-rated charge; no card -> checkout link the owner opens. The 'Owners and admins only' clause gives an eligibility precondition. It stops short of naming alternatives (e.g. where to check the current plan or credit balance), which is the only gap.

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?

Adds genuinely useful context beyond annotations: two formats are verified and returned links expire after 1 hour, which matters for an agent that may cache a URL. However it omits the key behavioral states (what is returned while rendering is pending or on failure) and there is mild tension between 'check ... return links' and readOnlyHint=false, which is never explained.

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?

One sentence with no filler, and the most important fact (link expiry) is included rather than buried. It is efficient but slightly under-specified in the same breath, so not maximally tight.

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?

No output schema exists, so the description is the only place the return shape could be described, and it covers only the success case (links) without pending/error states or the two-format structure. For a single-parameter, open-world polling tool this is adequate but leaves real 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?

Only one parameter (adaptation_id) and schema description coverage is 0%, so the description must carry the load; it only implies the ID refers to an adaptation and gives no format, origin (e.g. from list_adaptations), or behavior for invalid IDs. That is just enough to be workable but not compensatory.

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+resource: checking both video formats for an adaptation and returning download links. That clearly separates it from generate_adaptations and make_videos, though the phrase 'both video formats' is never defined, so the agent knows what is checked but not what the two formats are.

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?

'Once finished' implies this is a polling call to be used after generation, but the tool never says so explicitly, never names make_videos or generate_adaptations as the prerequisite, and gives no guidance on how often to poll or what to do while the adaptation is still processing.

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 this is a non-destructive, non-idempotent, closed-world mutation. The description adds a genuinely non-obvious behavioral fact not in any structured field: the operation is free and cost is deferred to generate_adaptations. It does not disclose duplicate-handling or what happens on repeated creation, but the cost disclosure is real added value.

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?

Two tight sentences, zero filler, with the action and its inputs front-loaded and the cost caveat placed second where it reads as a qualifier rather than a preamble.

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 11-parameter mutation with a nested language_choices object and no output schema, the description is thin: it omits any mention of the localization modes (bilingual/code-switch, visual text mode) that are the tool's main complexity. An agent could call it correctly on required params, but would have no basis for deciding whether to set language or visual options.

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 55%, so the description must carry some weight; it does map "source ad copy" to primary_text and "target audiences" to audience_ids, aligning with the two required semantic groups. However, it says nothing about language_mode, language_choices, visual_text_mode, or cultural_moment_id — the parameters most likely to confuse an agent — leaving them to bare enums and defaults.

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 campaign project") plus the two core inputs it wraps (source ad copy, target audiences), which distinguishes it from read-only siblings like list_projects. Differentiation is only partial — it never explicitly contrasts with the other creation-adjacent tools such as create_workspace or generate_adaptations beyond naming the latter as a follow-up.

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 sentence "no credits are spent until generate_adaptations is called" implies a workflow ordering (create here, then generate), which is useful, but it never states when this tool is the right choice versus its siblings or what prerequisites exist (e.g., brand/audience must exist first). 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?

Annotations already declare non-readOnly and importantly non-idempotent, and the description reinforces that by disclosing the trial/credit side effects and 'only needed once', which maps directly to the duplicate-creation risk. It does not state what happens if the user already has a workspace, nor what is returned.

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 that front-loads the action and packs in the trial terms and the once-only constraint with no filler. Nothing could be removed without losing meaning.

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-required-param bootstrap tool with full schema coverage and adequate annotations, the description covers purpose, cost, and call frequency. The remaining gap is post-call behavior — with no output schema, it does not say whether the created workspace/brand identifiers are returned for chaining.

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% with both required fields documented inline ('Company or agency name', 'First brand to adapt campaigns for'). The description's mention of 'workspace' and 'first brand' loosely maps to the two params but adds no format or constraint detail beyond the schema, so 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?

Specific verb plus two concrete resources ('Create the signed-in user's Kulture workspace and first brand'), scoped to the signed-in user and pinned to a 14-day trial with 5 credits. No sibling creates a workspace or brand-only onboarding entity, so the agent can disambiguate immediately.

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?

'Only needed once' clearly signals this is an onboarding/bootstrap call rather than a repeatable operation, which is the key usage context. It stops short of naming what to do instead if a workspace already exists (e.g. list_brands) or stating a failure mode on repeat invocation.

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 cover the safety profile (readOnly=false, destructive=false, idempotent=true, openWorld=true), and the description adds the non-obvious cost model and the fact that already-adapted audiences are skipped. That is meaningful context beyond the structured fields, though there's no mention of failure modes or what happens on partial credit exhaustion.

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?

Two sentences, no waste, with the resource purpose front-loaded before the cost constraint. Every clause carries 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 two-parameter generation tool with no output schema, the description covers purpose, cost, and idempotent behavior adequately. It could say more about the shape of the returned risk scores, but the essentials for a correct call are present.

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 50%; confirm_spend is documented in the schema as the credit-spend confirmation, while project_id is only implied by 'in a project'. The description adds no format or scoping detail for project_id, so it neither compensates for the gap nor repeats what the schema already says.

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 ('Write culturally adapted ad copy with risk scores') scoped to 'every audience in a project', which clearly distinguishes it from list_adaptations and review_adaptation. An agent can identify it as the generation action among siblings.

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 'for every audience in a project' plus the cost note 'Spends 1 Kulture credit per audience not yet adapted' tells the agent when this is the right call and what it costs. It doesn't explicitly exclude alternatives (e.g., single-audience paths or review_adaptation), so not a full 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?

Annotations already declare readOnlyHint=true, idempotentHint=true and openWorldHint=false, so the safety profile is covered. The description adds that the result is workspace-scoped and includes plan information, but says nothing about units, refresh cadence, or what a zero/absent balance means.

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; the resource being returned is named immediately.

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-param read there is little to explain, and the description names both return components (credits and plan) even without an output schema. Only the shape/units of those values are left unspecified, which is a minor 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?

Zero parameters, so the baseline is 4. The description correctly implies the tool needs no input because scope is derived from the workspace context.

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 (Show) and resource (remaining Kulture adaptation credits and plan) with the workspace as scope. An agent can distinguish it from the billing-side sibling buy_plan, though the description never names that contrast 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?

Usage is only implied: an agent can infer this is the pre-check before buy_plan or generate_adaptations, but no when-to-use or when-not statement is given. No exclusions or alternatives are mentioned.

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, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds that results carry risk scores, which is useful output context, but says nothing about pagination, ordering, or what happens when no adaptations 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?

A single front-loaded sentence with no filler. It is efficient, though slightly thin for a tool that returns scored results.

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?

There is no output schema, so the description is the only source on return values; it names 'risk scores' but not the shape of a result, ordering, or pagination behavior. Adequate to call correctly, but not complete for interpreting results.

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 limit and project_id are already documented with bounds, default, and format. The description only restates that the project filter is optional, adding no syntax or behavioral detail 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?

States a specific verb ('List') and resource ('culturally adapted ad copy with risk scores'), which clearly separates it from generate_adaptations (creates) and review_adaptation (single-item review). It doesn't explicitly name those siblings, but the read-vs-create distinction is implicit in 'List'.

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?

'optionally for one project' hints at the project_id filter use case, but there is no explicit guidance on when to call this versus generate_adaptations or review_adaptation, nor any stated prerequisite such as needing an existing project.

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, idempotentHint and openWorldHint=false, so the safety profile is covered. The description adds genuine domain scope (UK cultural audiences, a fixed catalog Kulture supports) and return content (IDs), but says nothing about catalog size, ordering, or whether the list is static.

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 waste; the resource and the returned identifier 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 zero-param listing tool this is nearly complete, but with no output schema the description should specify the full shape of each returned item (name plus ID, maybe description). It only mentions IDs, leaving the response shape partly unknown.

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 no parameter semantics to explain; baseline is 4. The description correctly signals a no-filter listing operation.

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 resource (UK cultural audiences Kulture can adapt campaigns for), plus what is returned (IDs). This is clearly distinct from siblings like list_brands or list_projects, though it does not explicitly name them.

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: the phrase 'with their IDs' hints that this is a lookup step to obtain audience identifiers for campaign adaptation, but the description never says when to call it or which sibling consumes those IDs (e.g. generate_adaptations).

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, idempotentHint=true and openWorldHint=false, so the safety profile is covered. The description adds that returned records carry tone of voice, banned words and mandatory terms, which is genuinely useful payload context, but says nothing about result size, ordering, pagination, or behavior when no brands 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?

A single sentence with no filler, and the resource is front-loaded immediately after the verb. Every clause (the three payload field names) earns its place by telling the agent what it will get back.

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 list tool with no output schema and annotations covering safety, the description gives enough to call it correctly and to know what the response contains. Only the empty-result/ordering behavior is unaddressed, which is minor at this complexity level.

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 and the baseline of 4 applies. The phrase 'the workspace's' correctly signals that scope comes from the session/auth context rather than an argument.

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?

Specific verb ('List') plus resource ('brands') scoped to the workspace, and it names the payload fields (tone of voice, banned words, mandatory terms), which goes beyond a restatement of the name. It does not need to differentiate from siblings since none of the listed tools touch brands, but it also does not say so 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 read-only enumeration nature makes the intended use implicit: fetch brand guidelines before generating brand-aware content. There is no explicit when-to-use, no prerequisites (e.g., that a workspace must already have brands configured), and no mention of alternatives.

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, idempotentHint and a closed-world scope, so safety is covered. The description adds genuinely new behavioral facts not in annotations or schema: results are scoped to the signed-in user and returned newest-first. It omits pagination behavior beyond the limit parameter, which is the main remaining gap.

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 zero waste, front-loading the action and scope, with the ordering detail appended where it belongs.

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, annotation-backed read tool with one documented parameter and no output schema, the description covers who/what/ordering. Slightly short of full completeness because it says nothing about the shape of returned project entries or paging beyond limit.

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 the single limit parameter is fully documented in the schema (max 100, default 25). The description adds no parameter semantics beyond that, so the 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+resource (list ... projects) plus scope (signed-in user's Kulture campaign projects) and ordering (newest first), which is enough to distinguish it from create_project in the sibling list. It does not explicitly contrast with other list_* siblings, but those cover different resources.

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 by 'the signed-in user's ... projects' – an agent can infer this is the read path for the current user's projects. There is no explicit when-to-use/when-not or prerequisite guidance, and no mention of an alternative to reach other users' or workspace-level projects.

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 it is not read-only, is open-world, and is idempotent; the description adds high-value context beyond that: cost of 3 credits, refund on failure, two aspect-ratio outputs, and asynchronous polling. One gap remains: with idempotentHint=true but credit spend involved, it does not clarify whether a retried call spends more credits, which is exactly the kind of ambiguity an agent needs resolved.

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 short sentences, each carrying useful information: scope and formats, cost/refund, and the polling instruction. The critical cost and async-follow-up facts are front-loaded rather than 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 paid async generation tool with no output schema, the description covers the essentials: duration, formats, cost, failure refund, and the polling tool. It could be more complete by stating that two video assets are produced per call and by clarifying retry/credit behavior, but nothing essential to a correct first call 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?

Schema coverage is only 50%: confirm_spend is documented in the schema, but adaptation_id has no description there. The description implies 'one adaptation' but does not explain what an adaptation ID is or where to obtain it, so it only partially compensates for the uncovered parameter.

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 ('Start an 8-second video ad'), plus the exact output scope: one adaptation rendered in both 9:16 and 16:9. It also names the sibling used for follow-up (check_videos), so an agent can distinguish this from generate_adaptations or process_image 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?

Gives clear context: this is a paid generation action that must be followed by polling with check_videos, and it operates on one adaptation. It does not explicitly say when not to use it (e.g., existing videos should be listed via list_adaptations instead), so the exclusion boundary is left 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?

Annotations declare readOnlyHint=false, openWorldHint=true and idempotentHint=true, so the safety profile is already structured. The description adds genuinely non-derived behavior: the call consumes the adaptation credit at no extra charge and takes roughly a minute, which matters for an agent deciding whether to block on it.

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?

Two short sentences with no waste; the precondition is front-loaded before the action and the cost/latency caveats trail it appropriately.

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 explains what the call produces conceptually (an adapted image plus risk reviews) and how long it takes. It does not say whether the result is returned inline or must be polled/fetched via review_adaptation or list_adaptations, which is the one remaining ambiguity for an agent.

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?

There is one required parameter (adaptation_id) with 0% schema description coverage and no description of its format or where to obtain it. The description partially compensates by tying it to 'an adaptation whose project has a source image', but the UUID semantics come only from the schema's format field.

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 concrete compound verb+resource: creates the adapted image and runs cultural visual risk reviews. This is clearly a mutation on a single adaptation, distinguishable from the sibling review_adaptation, though it doesn't explicitly say how it differs from generate_adaptations or review_adaptation.

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 precondition: 'For an adaptation whose project has a source image', which tells the agent when this tool is applicable versus not. It does not name alternatives (e.g. review_adaptation alone) or state what to do when there is no source image, leaving some routing 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?

Adds behavior beyond the annotations: decisions are logged in review history (audit side effect) and there's a hard validation rule blocking approval of culturally flagged visuals. That validation rule is not derivable from readOnlyHint=false, idempotentHint=true, destructiveHint=false. It doesn't describe what happens to the adaptation's state post-decision, so not a 5.

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 short sentences, front-loaded with the action, followed by the audit consequence and the hard constraint. Every sentence carries information an agent needs.

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 3-parameter mutation with annotations covering the safety profile and no output schema, this is nearly sufficient. Minor gaps remain around what the tool returns and whether an already-decided adaptation can be re-reviewed.

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?

With schema coverage at 33%, only the note field is documented in the schema, and the description restates its conditional requirement when requesting changes. The decision enum semantics (approved vs changes_requested) are clarified in prose, but adaptation_id gets no explanation of what it identifies or where to obtain it.

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 pair and resource: approve an adaptation or request changes, with the note requirement clarified. It's clearly distinguished from generate_adaptations and list_adaptations by being the decision step, though it doesn't name 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?

Gives a concrete eligibility gate — visuals that failed the cultural risk review cannot be approved — and clarifies the two decision paths. It lacks explicit when-not/alternative routing, but the preconditions are clear enough to act on.

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