Tintin Tryon

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

With no annotations, the description carries full burden and does so excellently: it states no account is needed, explains the scanning method (public product sitemap), defines concrete audit criteria (stale photos ≥180 days, season mismatches, retail-calendar windows), and describes the output (work orders with fixes and credit costs). This goes well beyond a vague 'audit' and gives the agent a clear behavioral model.

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 core purpose, and each subsequent sentence adds valuable specifics about method, criteria, and output. There is no filler or redundancy; every sentence 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?

Despite having only one parameter and no output schema, the description gives a complete picture: input (store URL), method (sitemap scan), audit dimensions, and output (work orders with fixes and cost estimates). It sufficiently covers the tool's behavior for an agent to select and invoke it correctly.

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 schema already provides 100% coverage for store_url, including an example ('examplestore.com'). The description adds context about Shopify and public sitemaps but does not add new semantics or constraints for the parameter. This meets the baseline for high schema 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 opens with a specific verb and resource: 'Audit a Shopify store's listing photos.' It clearly distinguishes this from sibling tools like create_preview_job or get_job_status by describing a read-only analysis action rather than a job creation or status retrieval.

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 usage: use audit_store to detect photo issues before creating try-on jobs, as it returns 'pre-filled create_tryon_job fixes.' However, it does not explicitly state when not to use it or name alternative tools (e.g., get_seasonal_guide) for seasonal insights. Usage guidance is present but only 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?

With no annotations, the description carries the burden and reveals key behaviors: free, one-per-store limit, no account needed, and async nature (polling). It does not mention error handling or what happens if limits are exceeded, but the disclosed context is substantial.

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 with no filler. Key information is front-loaded ('ONE free on-model preview per store, no account needed'), and every sentence 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?

Given no output schema, the description effectively covers purpose, usage prerequisites, constraints, and the next step (polling). It does not detail response format or error conditions, but the overall picture is adequate for an agent to invoke the tool correctly.

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 parameters are described. The description adds meaning by linking garment_image_url to an audit_store work order and specifying store_url relates to audit_store, providing relational context beyond the schema field descriptions.

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 generates a free 2K on-model try-on photo preview, using a specific verb ('generate') and resource. It differentiates from the paid sibling by emphasizing 'taste before buying credits,' making its purpose distinct.

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 provides clear context: use after an audit_store work order, one free preview per store, and poll with get_preview_status. While it implies the paid alternative ('taste before buying credits'), it does not explicitly name create_tryon_job or state when not to use this tool.

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 important behavioral details: credit cost (1 credit for 2K, 2 for 4K) and the API key requirement. However, it does not mention whether the operation is asynchronous, if it returns a job ID, or what happens on failure, leaving notable gaps.

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 concise and well-structured: a single purpose sentence followed by two supplementary details (cost and auth). Every sentence earns its place, and the front-loading makes the core function immediately clear.

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 description covers purpose, cost, and authentication, which are essential. However, with no output schema, it fails to state what the tool returns (e.g., a job ID) and whether it is asynchronous, especially important given the sibling get_job_status. It is adequate but incomplete for a create-operation.

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 high (88%), so the baseline is 3. The description adds useful context for garment_image_url ('e.g. straight from an audit_store work order') and links resolution to credit cost, but it does not significantly redefine or clarify parameters 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 clearly states the tool's function: 'Generate an on-model try-on photo from a garment/product image URL'. It uses a specific verb ('Generate'), specifies the resource ('try-on photo'), and gives a concrete use case ('straight from an audit_store work order'), which differentiates it from siblings like create_preview_job.

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 gives clear usage context by mentioning the typical source (audit_store work order) and the action (creating a try-on). It implies when to use this tool, though it does not explicitly contrast it with create_preview_job or state when not to use 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, the description carries the full burden of disclosing behavior. It only mentions the API key requirement and the output (balance and top-up link), but omits details like response format, error handling, or that this is a read-only operation. This is minimal disclosure for a financial/informational tool.

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 sentence with no wasted words, front-loading the core purpose. It is appropriately brief for a simple tool, though slightly under-specified in content.

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 no output schema and no annotations, the description is incomplete. It does not specify what fields the balance and top-up link appear in, any error conditions, or how the API key is passed. An agent would need more context to invoke the tool reliably.

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 schema has one api_key parameter with no description, and schema description coverage is 0%. The description adds that an API key is 'required,' which is useful but contradicts the schema's required: 0 designation, creating ambiguity. It does not explain key format or usage beyond the requirement.

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 provides 'Studio credit balance and top-up link,' which is specific enough to distinguish from siblings like get_job_status or get_preview_status. It lacks an explicit verb like 'retrieves' or 'returns,' but the name 'get_credits_balance' compensates for that.

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 guidance is given on when to use this tool versus alternatives. The only contextual note is 'Requires an API key,' which is a prerequisite rather than usage direction. There are no exclusions or references to sibling tools.

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 of behavioral disclosure. It adds useful details about successful responses (presigned URLs valid for 1 hour) and the API key requirement, but it does not explain behavior for failed or in-progress jobs, error conditions, or whether polling is a read-only 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 description is a single, efficient sentence that communicates the core purpose, success behavior, and a requirement in a compact way. It is front-loaded with the verb 'Poll' and every word contributes to understanding without unnecessary elaboration.

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, but the lack of an output schema and annotations means the description must fully explain the contract. It does mention the successful return value and API key, but omits what happens for non-successful statuses (e.g., still running, failed), which is critical for a polling 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% and there are two parameters (job_id, api_key). The description only mentions that an API key is required, adding minimal semantic value. It does not explain the format or purpose of job_id, nor how api_key is used for authentication, leaving the agent under-informed despite having 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 clearly states the tool polls a generation job and returns presigned output image URLs on success. It names the specific resource (generation job) and the action (poll), but does not explicitly distinguish from the sibling tool get_preview_status, which likely serves a similar purpose for preview jobs.

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 usage in a polling context after creating a generation job, but it does not explicitly state when to use this tool over get_preview_status or any other alternative. No exclusions or alternative guidance is provided, leaving the agent to infer the appropriate scenario.

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 of behavioral disclosure. It transparently states that on success it returns the preview image URL, that the URL is valid for 1 hour, and that no account is needed. However, it does not disclose behavior on failure (e.g., what happens if the preview is still processing), which is a meaningful gap for a polling tool.

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 extremely concise: two short sentences that deliver the key facts without redundancy. Every word adds value.

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 description covers the core purpose, successful return value, validity period, and auth requirement, but for a polling tool it lacks critical context about failure behavior and polling semantics (e.g., what a non-success response looks like, whether to retry, any rate limits). Given no output schema or annotations, this gap makes the description not fully 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 has one parameter (preview_id) with no description (schema coverage 0%). The description does not mention preview_id at all, leaving the agent to guess its purpose or format. Since the description must compensate for low schema coverage but doesn't, parameter semantics are weak.

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 action ('Poll a free preview') with a specific resource ('preview'), and the verb 'poll' distinguishes it from creation tools like create_preview_job and generic status tools like get_job_status. The purpose is immediately obvious.

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 usage by saying 'Poll a free preview' but does not explicitly state when to use this tool versus alternatives, nor does it mention prerequisites like creating a preview job first. There is no exclusion or alternative mentioned, so usage guidance is only implied.

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 burden. It discloses that no account is needed and that the calendar reflects current open/urgent status, which is useful behavioral context. However, it doesn't mention return format or error behavior, though the read-only nature is implied.

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, well-structured sentence that front-loads the core purpose and packs in key details (no account needed, what's included) without any fluff. Every phrase 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?

For a simple read-only tool with one optional parameter and no output schema, the description covers the main return content (open/urgent windows, deadlines, guide links) and audience context. It doesn't describe a data structure, but the output is clearly described enough for an agent to understand what it will receive.

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 a clear description of the optional category parameter. The tool description adds contextual value by mentioning per-category shoot guides, but it doesn't go beyond what the schema already says about the parameter's effect.

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 what the tool provides: a retail shoot calendar for fashion sellers, listing specific shoot windows, deadlines, and links to guides. This specific verb+resource distinguishes it from sibling tools like audit_store or create_preview_job.

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 when to use the tool — to check current shoot windows and deadlines for fashion sellers — and notes that no account is needed. It does not explicitly mention alternatives or exclusions, but the context is clear enough for an agent to select it appropriately.

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