Sketchlib

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

With no annotations, the description carries the full behavioral burden and does well: it discloses the return artifact (signed zip URL), the 1-hour validity window, per-call cost, and tier-dependent daily caps. It omits failure conditions (e.g., what happens when the cap is hit) and permission requirements.

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, zero filler, and the most decision-relevant facts — the signed URL and its 1-hour expiry — are front-loaded before the cost and cap details.

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 tool with no output schema, the description covers what is returned, how long it is valid, what it costs, and the rate cap — enough to call it correctly. Missing only edge behavior such as cap-exceeded errors or model-availability failures.

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 model_id parameter is fully documented in the schema with its source (search_models). The description adds no syntax, format, or sourcing detail beyond that, so the baseline of 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 precise verb and resource — returns a signed download URL for a model zip — and specifies the artifact format. This distinguishes it from get_model and search_models, which do not produce a downloadable artifact, without needing to 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?

The description conveys cost and rate-limit context (one credit, 100/day cap on subscription plans), which informs the decision to call it, but never states when to choose this over get_model or what conditions block a download. Usage is only implied by the word 'download'.

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. 'Return' clearly signals a safe read, and it discloses two concrete return fields, but it says nothing about error behavior for missing/invalid model_ids or whether access is restricted.

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

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

A single front-loaded sentence with zero filler; the resource and payload highlights come first.

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 single-record read with 100% schema coverage and no output schema, the description previews the returned metadata, which is enough for correct invocation. Slightly thin on error/empty cases.

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, and schema description coverage is 100% — the schema already explains model_id and where it comes from. The description adds no parameter detail 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 and resource ('Return full metadata for one model') with a sample of the payload (downloadable flag, preview image URL). It distinguishes itself adequately from search_models and download_model by being the single-record metadata fetch, though it doesn't explicitly name those 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?

No explicit when-to-use or when-not-to-use statement, but the schema's parameter description ('The model_id from search_models') implies the workflow: search first, then fetch details. Usage is inferable but not stated.

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

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

With no annotations, the description carries the full behavioral burden, and it does disclose meaningful traits: results are paginated metadata plus a preview image URL, has_more signals whether another page exists, and offset+limit retrieves the next page. It omits auth requirements, rate limits, and empty-result behavior, 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 with zero waste: purpose and filters first, then the return shape, then the pagination mechanic. Everything is front-loaded and 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, yet the description compensates by naming the return contents (paginated metadata plus preview image URL) and the has_more pagination flag. For an 8-parameter, 1-required search tool this is nearly sufficient; only error/empty-result behavior is unaddressed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented at the schema level (including limit bounds and the 3-character keyword minimum). The description only restates the filter categories and adds no syntax or format meaning 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 (search) and resource (Sketchlib 3D model library) plus scope (by keyword, optionally narrowed by author/category/tag/face-count). An agent can immediately distinguish this from get_model (fetch one) and download_model (retrieve a file).

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 phrase 'optionally narrowed by author, category, tag, or face-count range' implies when to add filters, and the pagination sentence implies iterative use. However, there is no explicit guidance on when to prefer this over get_model, nor any exclusions or prerequisites for use.

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