3DAssets.dev

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

With no annotations provided, the description fully discloses the behavior: no password is used, a verification code is emailed, and the API key is obtained via a follow-up call. The consent requirement is also stated.

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 few sentences but every sentence adds value: identity of the action, required inputs, a warning against passwords, the verification flow, and a consent requirement. No redundant or filler 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?

The description is complete for the agent's task: it explains what to request, what to avoid, the follow-up step (verify_account), and the consent condition. Since there is no output schema, return value details are unnecessary.

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% (name and username have descriptions; email and website do not). The description mentions only that username and email are required, but adds no detail about the optional name or website parameters, failing to compensate for the missing schema 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 verb 'Create a contributor account' and specifies the resource (contributor account) and target (the user). It distinguishes from sibling tools like verify_account by outlining the account-creation step.

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?

Provides explicit guidance: only needs username and email, never ask for a password, a 6-digit code is emailed, then call verify_account, and only proceed with explicit consent. This leaves no ambiguity about the next action.

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?

The description states that the tool submits the asset for processing and review, disclosing the primary side effect. It does not mention idempotency or failure modes, but for a submission action this level of transparency is reasonable.

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, concise sentence that conveys the essential action and context without extraneous detail. It is well-structured and easy to parse.

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 fits the two-step upload flow and references get_upload_url, giving appropriate context. It does not describe the post-submission process or response format, but this is acceptable for a finalize/submit tool and the schema covers required fields.

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 covers 67% of parameters with descriptions, but some parameters (quarantineKey, previewKey, tags) lack explanations. The tool description adds little beyond referencing the upload flow, so agents may not understand the origin or purpose of these fields.

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 role: after PUTting a file to a URL from get_upload_url, it describes the asset and submits it for processing/review. It effectively distinguishes this finalization step from the initial upload step.

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 a clear sequential condition ('After PUTting the file to the URL from get_upload_url'), implying when to use it. It does not explicitly compare with alternatives like submit_asset_from_url, but the sequential context is sufficient for most agents.

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 must carry the burden. It discloses the return content but does not state whether the operation is read-only, requires authentication, or has rate limits. Since the tool name implies a read, the lack of explicit behavioral notes is a minor gap but not misleading.

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 that immediately states the purpose and lists the contents. Every word earns its place; no filler or redundancy.

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 enumerates the key fields returned, which is substantial. It also clarifies the input (slug). Missing details like error cases or authentication are minor for a get-by-slug tool, and the sibling set provides enough context to infer typical usage.

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 description only repeats the parameter name ('by slug') without adding meaning beyond the schema, which already provides the pattern and length. With 0% schema description coverage, the description should compensate by explaining slug format or purpose, but it does not.

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 a specific verb ('get') and resource ('asset'), and enumerates the returned details (CDN URL, licence, stats, etc.), which distinguishes it from siblings like get_asset_usage and get_pack. It is specific and unambiguous.

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 (retrieve full asset details) but does not explicitly state when to use it versus alternatives like get_asset_usage or search_assets. There is no exclusion or comparison to siblings, leaving the routing decision to the agent.

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 must disclose behavioral traits. It only states the functional output (code/steps) without mentioning side effects, auth requirements, rate limits, or whether it is read-only. As a 'get' operation it is likely safe, but this is not disclosed.

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, direct sentence with no fluff. It is appropriately sized and front-loaded with the core purpose, making it easy to parse.

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, no annotations, and 0% parameter coverage, the description is severely incomplete. It fails to explain what the output looks like, the role of the 'slug' parameter, or any usage context. An agent would struggle to correctly invoke this tool without external knowledge.

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. It lists the frameworks (matching the enum for the 'framework' param) but does not explain the 'slug' parameter or its format. It adds minimal meaning beyond the schema, leaving the agent to guess what slug refers to.

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 it returns code or steps to load an asset, listing specific frameworks. This distinguishes it from sibling tools like get_asset (which likely returns the asset itself) and get_demo. The verb 'get' and resource 'usage' are explicit and unambiguous.

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 provides no guidance on when to use this tool versus alternatives such as get_asset or get_demo. It does not mention any conditions or exclusions, leaving the agent to infer its purpose from the name alone. There is no explicit when/when-not guidance.

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 fully discloses behavior. It lists every piece of data returned, provides units (metres, degrees), and even instructs how to load models (GLTFLoader with transforms). This goes beyond typical transparency and gives the agent actionable details on output structure and usage.

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, information-dense sentence that front-loads the purpose and then lists components and usage. It is appropriately sized for the complexity of the output, with no wasted words. The structure is logical and easy to scan.

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 there is no output schema, the description fully specifies what the tool returns and how to interpret it. It covers the pack, spawn point, bounds, points of interest, and each placement with coordinates and model URLs, plus instructions on loading. Nothing needed for a correct call is missing, except maybe error handling, which is not essential for a getter.

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 parameter 'slug' with format constraints but no description, and schema coverage is 0%. The description does not explicitly explain that 'slug' identifies the demo, though it is implied by 'Get one demo'. The pattern and length hint at an identifier, but the description fails to add semantic meaning or clarify the expected format 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 fetches a single demo and enumerates the exact contents: pack, spawn point, walkable bounds, points of interest, and each placement with CDN URL and bounds. It distinguishes from siblings like get_pack (pack metadata) and list_demos (listing) by focusing on the complete demo layout for reproduction.

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 when you need a specific demo's layout, but it doesn't explicitly state when to use this tool over alternatives or when not to use it. There is no mention of list_demos for discovering demos or get_pack for pack-only data. The context is clear but exclusions are 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 must disclose behavioral traits. It explains the manifest contents and that it's free, but it does not mention authentication requirements, error behavior, response format, or side effects (though it's presumably read-only). It covers the content well but leaves process details unspecified.

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 two sentences, front-loaded with the core action, and lists details efficiently 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?

For a simple retrieval tool with one parameter and no output schema, the description lists the returned components, which is helpful. However, it omits the meaning of the slug parameter and any response format or error handling, leaving some gaps 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?

The only parameter 'slug' is not described in the description. The schema gives pattern and length constraints but no semantic meaning. Since schema description coverage is 0%, the description should explain that slug is the pack's unique identifier, but it does not.

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 states a specific verb ('Get') and resource ('free pack manifest') and enumerates the contents (CDN URLs, licences, geometry budgets, loader snippets, starter scene). It clearly distinguishes from sibling tools like get_asset by focusing on packs, not individual assets.

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 provides no explicit guidance on when to choose this tool over alternatives like get_asset or search_packs. It mentions downloading models or using the starter scene, but that's usage of the returned data, not tool selection criteria.

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?

Without annotations, the description carries the full burden. It accurately describes the return value (presigned PUT URL) and the optional preview behavior. It does not mention potential side effects (e.g., creating a pending upload record) but otherwise is transparent about observable behavior.

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 concise sentences with no redundancy. The core action, optional behavior, and follow-up are all included without unnecessary detail.

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, mentioning that the call returns keys and that finalize_upload should be called next provides adequate context for a caller. It lacks error scenarios or caveats, but the essential workflow is complete enough for basic usage.

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 provides types, defaults, and constraints for all three parameters. The description adds meaning to withPreview (PNG preview) but does not elaborate on filename or contentLength. Since schema coverage is high, the baseline is 3; the description adds only minimal extra semantic detail.

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?

Clearly states the primary function: returns a presigned PUT URL for a .glb file, optionally with a PNG preview. Also indicates the follow-up action (call finalize_upload), so the caller understands the tool's role in the upload workflow.

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 'For clients that can upload bytes' identifies when this tool is appropriate, and the instruction to call finalize_upload afterward provides a usage sequence. It does not explicitly contrast with sibling tools, but the context is sufficient for a typical caller.

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?

The description discloses a specific behavioral guarantee: 'Unavailable suggestions never block uploads.' However, it does not explicitly state whether the tool is read-only or has any side effects, and there are no annotations to clarify.

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, consisting of three focused sentences that efficiently convey key characteristics without unnecessary detail.

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 the minimal schema and lack of annotations, the description covers the primary purpose and some behavioral aspects, but it does not describe the output format, potential errors, or explicit parameter usage, leaving some gaps 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?

The schema provides only a parameter 'q' with a maxLength but no description. The description indirectly indicates that 'q' is used for searching model/tool names, but it does not explicitly map the parameter or provide examples.

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: searching model suggestions from OpenRouter. The verb 'search' and the source 'OpenRouter' make the purpose unambiguous.

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 provides some guidance by noting that custom model/tool names are accepted and that the list is not exhaustive, but it does not explicitly mention when to prefer this tool over related search 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?

The description only states that categories are listed; it does not mention side effects, permissions, data access, pagination, or any behavioral characteristics. Since there are no annotations to fill this gap, the agent gets minimal transparency about what the tool actually does beyond the surface action.

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, concise sentence that stays on topic. It includes a valuable parenthetical about slugs without adding unnecessary words or complex structure.

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 tool with no output schema and no parameters, the description adequately states the purpose and adds the key fact that returned slugs are used by other tools. It could be more complete by describing the output shape (e.g., array of category objects), but the current level is sufficient for a simple listing endpoint.

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 zero parameters, so the baseline is 4. The description adds no parameter-specific details, but there are no parameters to describe. The note about slugs relates to output, not input, so it does not alter the parameter semantics score.

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 verb 'List' is unambiguous and the object 'categories' on '3dassets.dev' is specific. This clearly distinguishes it from sibling tools like list_tags, list_licenses, and list_demos, which name different entity types.

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 'slugs are what other tools accept' hints that this tool is used to obtain slugs for subsequent calls, but it never explicitly states when to use this over alternatives. No conditions or direct comparisons to sibling tools are provided.

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. It describes a read-only list operation and implies that detailed placements are handled by get_demo, giving useful behavioral context. It does not explicitly state side effects, but listing is naturally safe.

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 two sentences, front-loaded with the core purpose, and includes a helpful pointer to get_demo. No wasted words.

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?

It defines what a walkable demo is (a small environment to walk through and load as a layout) and clarifies the boundary with get_demo. For a parameterless list tool, this is 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 schema has zero parameters, so there is nothing to describe. The baseline for 0 params is 4, and the description adds no unnecessary or missing info regarding 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 lists walkable demo scenes built from single packs, and explicitly differentiates it from get_demo by directing users there for placements. This gives a specific resource and action.

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 explicitly says 'Use get_demo for the placements,' which tells the agent when to use this tool versus the alternative. This is clear usage guidance.

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?

Without annotations, the description carries the full burden of behavior disclosure. It states the return value (CC0 1.0) and its implication (no selection needed), providing good transparency, though it could mention the exact output format.

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 two short sentences, directly to the point, with no wasted words or redundant phrases.

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 fully explains the tool's result and its practical implication (no selection needed), but does not specify the response structure or any error conditions, which is a minor gap given the tool's simplicity.

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 has zero parameters, so the schema covers 100% and the description adds no parameter-specific information. This falls to the baseline of 3.

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 returns the sole accepted submission dedication (CC0 1.0 Universal) and explicitly notes that no license selection is needed, leaving no ambiguity about its purpose.

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 explains what the tool does but does not explicitly mention when to use it versus alternatives. However, given that no sibling tool appears to handle licenses, the usage context is largely 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 provided, the description carries the full burden. It correctly implies a read-only operation ('list') but does not disclose response format, pagination, or ordering. The slug note adds some behavioral context, but overall the description is minimal for a tool with no structured safety metadata.

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 zero waste. It front-loads the purpose and includes a valuable parenthetical about slugs. Every word 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 parameterless list operation, the description is largely complete. It states what the tool does and hints at downstream usage. The absence of an output schema means the return format is unspecified, but for a simple tag list this 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?

There are zero parameters, so the schema covers 100% of parameters vacuously. Per the rubric, the baseline is 4, and the description adds the useful slug context that supports parameter usage in other tools.

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 verb 'List' and the resource 'tags' on the specific domain '3dassets.dev'. It also adds a distinguishing detail: 'slugs are what other tools accept', which separates this from sibling list tools like list_categories or list_ai_models.

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?

While it doesn't explicitly state when to use this vs. alternatives, the slug note implies the tool is used to obtain slugs for other tools. This provides clear usage context without being overtly comparative, but it lacks explicit 'when not to use' guidance.

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?

The description explains the meaning of the 'pendingEdit' status, which is a behavioral nuance, but it does not explicitly state whether the operation is read-only or if it has any side effects, permissions, or other behavioral aspects. Since it is a listing, it is likely read-only, but that is not stated.

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, with two sentences that provide necessary information without unnecessary fluff. It is 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?

The description is fairly complete for a simple listing tool. It explains the purpose and clarifies a special status value. However, it does not describe the output format or any other contextual details, but given the simplicity, it is adequate.

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 are no parameters, so the baseline is 4. The description does not need to explain parameters as none exist, and it does not add anything beyond the schema for 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 action (list) and the object (user's submissions), and enumerates the possible statuses, making the purpose unambiguous.

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 does not explicitly state when this tool should be used relative to other tools, nor does it provide any conditions or recommendations for usage. It only describes what it does, leaving the agent to infer when to call 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?

No annotations are provided, so the description carries the full burden. It accurately describes a read-only listing operation without hinting at side effects or mutations. The description is transparent about what the tool returns.

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, compact sentence that includes all essential information without any fluff. It is well-structured and immediately understandable.

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?

Even without an output schema, the description specifies the key return elements: status, pack URL when approved, reason when rejected, and member model status. This is sufficient for an agent to know what to expect.

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 has zero parameters, and with 100% schema coverage, the description does not need to explain any inputs. Baseline score of 4 applies given no parameters exist.

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?

Clearly states the verb 'List' and the resource 'user's pack proposals', and distinguishes itself from other pack-related tools by focusing on the user's own proposals with status details. The inclusion of member model status adds further specificity.

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 makes it clear that this is for listing the user's own pack proposals, but it does not explicitly contrast with sibling tools like 'search_packs' or 'get_pack'. The intent is inferable, though not as explicit as the ideal example.

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?

The description discloses key behavioral aspects: it returns direct CDN URLs (CORS *, immutable) that can be loaded with a GLTFLoader, and it states that licence metadata is retained in results. This goes beyond simple read-only semantics, but it does not mention pagination behavior or default sorting nuances.

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 reasonably concise and front-loaded with the core purpose. The additional sentences about CDN URLs, licenses, and metadata add context without excessive verbosity, though it could be slightly tighter without losing 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?

For a search tool with no output schema, the description provides sufficient context: it explains what is searched, what is returned (CDN URLs), and the license implications. It lacks details about result structure or sorting options, but overall it gives enough for an agent to understand the tool's role.

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 description clarifies the meaning of the main search parameters (query, category, theme, style) and ties them to user-facing concepts. However, it does not add information about the page or limit parameters, which lack descriptions in the schema, so not all parameter semantics are covered.

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: searching the free GLB catalogue on 3dassets.dev, with explicit mention of the search dimensions (text, category, theme, style) and the output (CDN URLs). It distinguishes this from pack-level searches by focusing on individual GLB assets.

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 does not provide any guidance on when to use this tool versus alternatives like search_packs or list_tags. It implies it is the tool for searching individual assets, but offers no explicit conditions or criteria for tool selection.

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 the return contents (file counts, total bytes, starter scene URLs) and implicitly indicates a read-only search operation. It does not mention pagination or result limits, which would add further transparency.

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 concise sentences deliver purpose, return content, and follow-up guidance with no redundant wording. The structure front-loads the main action and then adds relevant 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 search tool with no output schema, the description adequately specifies what results look like (file counts, total bytes, starter scene URLs) and directs the agent to get_pack for deeper model-level details. This is sufficient for an agent to decide when and how to use the 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 covers all 4 parameters with descriptions, so baseline is 3. The description adds value by mapping its search dimensions ('text, theme or style') to the query, theme, and style parameters, and clarifies that q is an alias. It does not introduce extra semantic constraints 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 states a specific verb ('Find'), a clear object ('cohesive free GLB game kits' = asset packs), and the search dimensions (text, theme, style). It distinguishes from the sibling search_assets by focusing on packs, and names get_pack as the follow-up for individual models.

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 explicit guidance to use get_pack for individual model and loader details, implying search_packs returns pack-level summaries. However, it does not directly contrast with search_assets to clarify when to choose one search over the other.

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?

The description discloses key side effects: downloading the asset, validating and re-encoding it, requiring CC0 acceptance, and deferring publication until human review. It does not discuss potential failure modes or async responses, but it clearly communicates the submission lifecycle and irrevocable dedication requirement.

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 compact and focused, conveying the entire submission workflow and key constraints in three sentences. No redundant or irrelevant information is included, and the structure flows logically from action to process to requirements.

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 provides enough context for an agent to understand the full submission process: source URL, technical requirements, license implications, API key requirement, and the human review step before publication. It is complete given the schema already documents individual parameters and there is no output schema to describe.

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?

While individual parameters are mostly documented in the schema, the description adds important global constraints not present in the schema: 25 MB max size, 1 million triangle limit, embedded textures only, and the need for a public https URL. This supplements the schema meaningfully, though it does not detail each parameter individually.

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 submits a .glb file from a public https URL on the user's behalf, distinguishing it from pack submission and other asset operations. It also specifies the validation, re-encoding, review, and publishing workflow, making the tool's purpose unambiguous.

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 explains when to use the tool: for submitting .glb assets from a URL, with requirements like public https URL, API key, size limits, and embedded textures. It could more explicitly contrast with sibling tools like submit_pack, but the .glb-specific scope and constraints provide clear practical guidance.

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 thoroughly. It discloses that each model is reviewed individually, the pack is approved as a whole, nothing goes live until approval, and the user is emailed the outcome—providing rich behavioral context beyond the schema.

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 four sentences with no filler, front-loading the core action before detailing the workflow. Every sentence adds value, from the count constraint to the approval process to the email notification.

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 prerequisites, the approval flow, publication behavior, and notification. For a tool with no output schema, it provides sufficient context for an agent to invoke it correctly, including the state of uploads (processing, pending, published).

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. It explains the assets parameter (slugs of the user's own uploads, 2–50) but does not elaborate on title, summary, or description. The schema itself marks these as required and constrains them, but the description adds limited semantic value beyond the assets 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?

The description clearly states a specific action: grouping the user's own uploads into a pack proposal, with explicit constraints on count (2–50). It distinguishes itself from sibling upload tools by positioning as the pack-proposal step after uploading, making its purpose unambiguous.

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 explicitly instructs the agent to upload models first via finalize_upload or submit_asset_from_url, then pass slugs. This establishes a clear workflow and implies this tool is not for individual uploads. It stops short of explicitly stating when not to use it, but the guidance is strong.

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 transparency burden and discloses key side effects: uploaded GLB is immutable, published edits go to the review queue, and the live version remains until approval. It does not mention auth failures, rate limits, or other error conditions, but the core behavior is well covered.

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 two concise sentences with no filler or redundancy. It efficiently communicates the main action and the most important constraints.

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 provides enough context for an agent to understand the main workflow, including the review queue behavior for published assets. It does not explicitly state whether unspecified fields are left unchanged or describe the return value, but these are not blocking 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?

Schema description coverage is only 25%, and the description does little to explain individual parameters beyond their generic names. aiModel and category have some schema-level descriptions, but tags, summary, description, aiGenerated, and slug are left largely to inference.

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 a distinct operation: editing metadata of the user's own assets. It also differentiates the tool by emphasizing that the GLB cannot be changed and that published edits go through review, which helps separate it from upload/create tools.

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 this tool: for metadata-only edits. It also provides an important exclusion by stating the GLB can never be changed, though it does not explicitly name sibling tools such as submit_asset_from_url for comparison.

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?

The description transparently indicates the operation is an exchange that yields an API key, and instructs the user to display it. No contradictory side effects are mentioned, and the read-only nature (no data modification) is implied, though not explicitly labeled as safe.

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—two sentences with no redundant information. It front-loads the core action and immediately follows with the expected follow-up action, making it efficient for the agent to parse.

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?

It covers the primary function and the required user-facing action, making it sufficient for an agent to invoke the tool correctly. It omits potential edge cases or error handling, but these are not essential for basic usage.

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 description clarifies that 'code' is the 6-digit emailed verification code and 'email' is the account email, adding meaning beyond the bare schema. It does not elaborate on validation or error conditions, but the essential purpose of each parameter is conveyed.

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 (exchange code for API key) and the intended outcome (show key as Bearer token). It is distinct from sibling tools like create_account or get_asset, focusing specifically on account verification.

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 guidance on when to use (after receiving the emailed code) and what to do with the result (configure as Bearer token). While it doesn't explicitly rule out alternative scenarios, the context is sufficient for typical use.

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