Sketchlib

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

With no annotations, the description carries the full disclosure burden and does well: it reveals the 7-day expiry, the credit cost, and the ownership exemption that makes re-download free. It omits error behavior (e.g., what happens with insufficient credits) and any auth requirements, keeping 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?

Three tight sentences, ~20 words, with the core outcome and validity front-loaded and the pricing caveats after. No filler and nothing redundant.

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?

Absent an output schema, the description usefully explains what is returned (a signed URL) and its lifetime, plus the credit model. Failure modes and permission requirements are unaddressed, but for a one-parameter download helper this is close to sufficient.

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 single parameter has 100% schema description coverage and even cross-references search_models as its source, so the schema does the heavy lifting. The description adds no additional meaning about the identifier's format or origin — the baseline 3 for high-coverage schemas.

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 ("Return a signed download URL") and resource ("model zip") with an exact validity window, so an agent knows precisely what it gets back. It never names or contrasts sibling tools such as get_model or get_model_preview, so sibling differentiation is left to inference.

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?

Cost information ("Consumes one credit", owned models are free again) is decision-relevant guidance, but there is no explicit when-to-use statement, no prerequisites, and no routing among the many siblings (get_model, get_model_preview, generate_model).

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 does so: asynchronous with a 3-7 minute runtime, immediate task_id return, credit charging with server-computed credits_cost, pre-flight refusal when unaffordable, and a privacy guarantee that results are visible only to the calling account and stay out of the public library.

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 dense block, front-loaded with the what (generate GLB from one photo) before the async/credit/parameter mechanics. Nearly every clause carries information, though the precedence sentence partially duplicates the schema.

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 async generation tool with no annotations and no output schema, the description still covers the full lifecycle an agent needs: initiation, the returned task_id, polling via get_generation, cost behavior, failure-before-work, and result visibility.

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 the baseline is 3, but the description adds genuine behavior beyond the schema: the 'omit both and the user uploads through their browser' mode is not stated anywhere in the schema. It also re-states the base64-over-urls precedence the schema already documents, which is redundant but not harmful.

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+resource ('Generate a 3D model (GLB)') and pins the input modality to 'one photo of an object'. An agent can distinguish it immediately from read-side siblings like get_generation, get_model, and download_model.

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 operating context: supply image_base64 or image_urls to generate now, or omit both for browser upload, and it names get_generation as the progress-polling follow-up. It lacks an explicit 'use this instead of X' exclusion, but no sibling competes for the same generation job.

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 and does well: it discloses the status state machine (pending/running/done/failed), the 7-day download URL validity, and that credits are auto-refunded on failure with the reason in the error field. It omits polling intervals, rate limits, and auth requirements, so it is not fully exhaustive.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, each front-loaded and load-bearing: purpose first, polling behavior second, terminal outcomes third. No filler.

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 must explain the response, and it does: download URL on success, error explanation plus automatic refund on failure. An agent has everything needed to call and interpret this 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 coverage is 100% and the single task_id parameter already documents that it comes from generate_model, so the description adds no syntax or format meaning beyond the schema. 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?

States a specific verb and resource ('Report the status of a generate_model task') and explicitly scopes it to the generate_model workflow, which cleanly separates it from siblings like get_model, download_model, and list_generations.

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 contextual guidance: poll after generate_model, and back off while status is pending/running. It does not name an alternative tool or state when not to use this one, so it falls short of an explicit routing rule.

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, and it usefully discloses a data-quality caveat: the thumbnail URL is hosted by the source platform and is missing for some models. It also implies a read-only metadata fetch and flags the downloadable field, though it omits auth requirements, error behavior for an unknown model_id, and overall response shape.

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, front-loaded with the primary behavior and the return caveat, with zero filler. Each 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?

There is no output schema, so the description must describe the return; it names only two of the 'full metadata' fields and leaves the rest vague. Combined with no annotations, an agent still lacks a complete picture of what comes back.

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 model_id parameter is already documented as coming from search_models, so the description adds no parameter meaning beyond the schema. Baseline 3 is appropriate for a one-param tool with full 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?

States a specific verb and resource ('Return full metadata for one model') and even names the notable fields returned. It partially differentiates from siblings by pointing to get_model_preview, though it does not distinguish itself from search_models or download_model.

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 routes the agent to get_model_preview but only for one narrow case (an alternative preview image), not for deciding when to use get_model at all. There is no explicit when-to-use or when-not-to-use guidance relative to search_models or download_model.

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 and does real work: it discloses the return type (an image), that it consumes one of the same reads as search_models, and that it counts against the same per-minute limit. That quota/rate-limit disclosure is exactly the kind of behavioral context annotations would otherwise provide; only failure modes (e.g., no preview available) are unaddressed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three tight sentences, front-loaded with what is returned, then purpose, then cost/quota constraints. Every sentence earns its place with no repetition of the title or schema.

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 single-parameter read tool with no output schema, the description covers return format (an image), intended use, and the credit/rate-limit cost — the full set of facts an agent needs to call 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?

Schema description coverage is 100% with a single required parameter already documented as 'the model_id from search_models'. The description adds no format or sourcing detail beyond the schema, 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 and resource — returns the preview image of one model as an image — and adds the purpose (show to user or judge before downloading). An agent can distinguish this from download_model and get_model without opening any schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives a clear when-to-use condition: verify a model from search_models is the one the user means before spending a credit. The alternative (downloading) is implied by 'before spending a credit on it' rather than named explicitly, so it falls just short of the explicit-alternative bar.

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 and does well: it discloses account scoping, newest-first ordering, the entry status field, and that 'done' entries carry a GLB download URL valid for 7 days. It does not describe pagination behavior against the limit parameter or what non-'done' entries contain, leaving some 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?

Two sentences, front-loaded with purpose, followed by return-value behavior and then usage guidance. No filler; every clause 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?

There is no output schema or annotations, so return-shape disclosure matters, and the description supplies the key facts (entries per task, status, download URL, 7-day validity). It leaves the fuller entry shape (e.g., task_id/model identifiers) implicit, so an agent can still act but with minor uncertainty.

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 (limit) at 100% schema coverage, so the schema fully documents it (default 20, max 50). The description adds no syntax or semantics beyond the schema, which is the baseline-3 case.

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 ('Report') and resource (past generate_model tasks) scoped to the calling account, with explicit ordering (newest first). This clearly distinguishes the list operation from the singular get_generation and the create-side generate_model 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?

Gives an explicit when-to-use: 'Use this to find 3D models generated earlier when the task_id was lost.' That is a concrete trigger. It stops short of naming alternatives (e.g., get_generation for a known task_id) or when-not-to-use, so it falls just short of 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?

With no annotations provided, the description carries the full burden and does well: it discloses the return shape (paginated metadata plus a preview image URL) and the pagination contract (has_more indicates a further page; raise offset by limit to fetch it). It omits auth/rate-limit or result-cap 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?

Three tight sentences: purpose and filters first, then return content, then the pagination recipe. No filler, and the most actionable detail (offset += limit) is saved for last 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?

There is no output schema, so the description must explain returns, and it does: paginated metadata, preview image URL, and the has_more/offset contract for paging. An agent can call this correctly and iterate pages without further documentation.

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 every parameter including the 3-character keyword minimum and the 1-50 limit range. The description restates the filter dimensions (category, tag, face-count range) without adding syntax or matching-behavior detail beyond the schema, which is the baseline 3 case.

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 (Search) and resource (Sketchlib 3D model library) and enumerates the narrowing dimensions: category, tag, face-count range. The read-oriented search framing is clearly distinct from the get_model/download_model siblings by name, though the description never explicitly contrasts itself with 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 implied by 'Search the Sketchlib library by keyword, optionally narrowed by...' which tells the agent this is the discovery entry point, but there is no explicit when-to-use vs get_model/download_model guidance and no stated prerequisites or exclusions.

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