SmartTokenGuard

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

With no annotations, the description carries the full burden of behavioral disclosure. It only restates return fields (plan, checks used/remaining) that are already covered by the output schema, and says nothing about read-only safety, auth requirements, or rate limits.

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 fragment that is concise but under-specified and not structured as a proper sentence. It fails to front-load the action or purpose, leaving the agent without a clear operational takeaway.

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 no-parameter tool with an output schema, the description still needs to clarify purpose and usage. It does not explain what the tool does or when to call it, making it incomplete despite the structured schema support.

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 for parameter semantics is 4. The description adds no parameter-related meaning, which is acceptable given there are no inputs.

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 is a noun phrase ('The license's plan, checks used and checks remaining this month') that describes returned data but not the tool's action. It lacks a verb like 'get' or 'retrieve' and does not distinguish this tool from siblings like stg_pricing or stg_qa_gate.

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 guidance on when to use this tool versus alternatives. The description gives no context, exclusions, or conditions for invocation, leaving the agent to infer usage.

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 disclosure burden and does so well: it reveals the 32 MB / 60 s / 1080p input ceilings, the cost model ('Uses 1 check'), and the return shape (JSON report plus review frames). It does not mention authentication or rate-limit behavior, which keeps it short of 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?

Front-loaded with the core action and constraints, and every clause carries information (defect list, limits, cost, return, workflow). The mid-sentence defect enumeration is dense but not wasteful; the whole thing is one efficient paragraph.

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 or annotations exist, so the description correctly fills the void with limits, cost, return content, and workflow ordering relative to stg_qa_gate. The remaining gap is parameter-level meaning for sensitivity/review_frames/media_url, which leaves it slightly short of complete for a 4-param 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 0%, so the description must compensate for all four parameters. It gives context for reference_image_url (drift comparison target) and implies review_frames (frames are returned), but media_url is only implicit and 'sensitivity' is never explained, leaving half the parameters semantically ungrounded.

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 precise verb (measure/inspect) and resource (rendered image or video at a public https URL) and enumerates the exact defects detected: blur, exposure, black/frozen frames, flicker, cuts, impacts, and colour/lighting drift. It also positions itself relative to the sibling stg_qa_gate, so an agent can distinguish it 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?

The description establishes the workflow slot clearly: run this 'before calling stg_qa_gate' and use the returned frames to check anatomy/identity defects. It gives input constraints (max 32 MB / 60 s / 1080p) but stops short of explicit when-not-to-use guidance or naming an alternative for oversized/long media.

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 behavioral burden. It does disclose one useful trait - no license is required to call it - but says nothing about side effects, whether it is read-only, or whether it implies any checkout/authorization action, which is a notable gap given the checkout-links wording.

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 terse sentence, front-loaded with the useful content and free of filler. The phrasing is slightly telegraphic ('Plans, prices and checkout links') 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 an output schema present, return values need not be described, and with zero parameters the schema burden is nil. What remains is an adequate but thin definition: the auth note is covered, yet the nature of the operation and its role among the stg_* siblings is left implicit.

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; the schema has nothing for the description to elaborate on. No parameter-level meaning is missing.

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 lists what the tool surfaces (plans, prices, checkout links) which gives a reasonable sense of the resource, but offers no verb and never states the operation, so the agent must infer that this is a lookup/model-retrieval tool. No sibling overlap is apparent, but there is no explicit differentiation either.

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 when-to-use guidance, no mention of when another sibling (e.g. stg_account or stg_spend_report) would be the better choice, and no task context. The only usable hint is 'No license needed', which is a precondition rather than usage direction.

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 it does add two real behavioral facts: the tool is stateless (caller must supply history) and it costs nothing to call. It omits the decision thresholds, side effects, and whether it consumes budget counters, so for a budget/fallback-routing gate the disclosure is only partial.

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 padding, but the ordering is poor: it front-loads the enum of verdicts rather than the tool's purpose, so the most orienting information arrives last. It is compact without being well-structured.

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, budget-aware gate with zero schema coverage and no annotations, the description is far too thin — it does not explain the cost/budget inputs, the stage distinction, or how defect inputs drive the verdict. The presence of an output schema excuses it from documenting return values, so the verdict list is arguably redundant, but the input-side gaps remain.

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% across 11 parameters, so the description must compensate. It explains only shot_history and its nested 'record_this_attempt' shape; model, render_cost_usd, stage, semantic_defects, technical_issues, project_spent_usd, project_budget_usd, visually_reviewed, fallback_model, and notes are all left undocumented anywhere.

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 never states a verb+resource; it opens with the verdict vocabulary (PASS / NEEDS_VISUAL_REVIEW / RETRY_SAME_MODEL / ...) so an agent must infer that this is a QA gate that evaluates a render and returns one of those decisions. The name plus the verdict list make the purpose guessable, but it is never explicitly articulated and there is no differentiation from siblings (which are unrelated utility tools anyway).

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 one concrete usage rule — 'Stateless: pass earlier attempts for this shot in shot_history' — which tells the caller how to get correct behavior across retries. It does not say when each verdict is emitted, what conditions trigger HALT vs HALT_BUDGET, or when to prefer this tool over alternatives, so the 'when to use' story is implied at best.

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