What's That Model?

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

Annotations already establish read-only, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds modest value by implying the output is a curated/shortlist style rather than exhaustive, but says nothing about how constraints are validated or what 'verified' means operationally.

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 efficient sentence with the core action front-loaded and no filler. It is not padded, though its brevity comes at the cost of substance rather than from crisply conveying 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?

For a tool whose second parameter is an undocumented nested object and which has no output schema, the description leaves too much unsaid. An agent cannot confidently construct constraints or predict the shortlist's shape from this text alone.

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?

With 0% schema description coverage and no property definitions for the nested 'constraints' object, the description needed to compensate and does not. It alludes to 'verified constraints' but never explains what keys or formats the nested object accepts; only the self-descriptive enum values for use_case carry meaning.

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 names a verb ('Get') and a resource ('editorial shortlist') tied to a use case, so the general intent is discernible. However, 'editorial shortlist' and 'verified constraints' are vague jargon, and nothing distinguishes it from the overlapping siblings find_model, list_models, or compare_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?

The phrase 'for a known use case' weakly implies you should already have a use case in mind, but there is no explicit when-to-use guidance, no exclusions, and no named alternative (e.g., vs find_model for free-text search). An agent must guess which sibling to pick.

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?

Annotations already declare readOnlyHint, idempotentHint, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description adds one genuinely useful behavioral trait – comparison is limited to documented capabilities and unknowns are surfaced rather than filled in – but says nothing about how results are ordered, how ties are handled, or what the response looks like.

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

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

Two short sentences with zero filler, and the core action is front-loaded. It is efficient, though the terseness shades into under-specification rather than tightness.

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 parameter descriptions, and a 10-word description, key calling information is missing: what form model identifiers take, what the comparison returns, and how 'task' influences the result. The description is far too thin for a two-parameter analytical 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% for two parameters. The phrase 'task fit' only indirectly signals that the 'task' parameter shapes the comparison, and the 'models' array is never described: no identifier format, no mention of the 2–4 item bounds, no ordering semantics.

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 'Compare' is specific and the resource is inferable from the tool name (models), with the comparison dimensions named ('documented capabilities and task fit'). However, it never explicitly names its closest sibling best_model_for or distinguishes itself from find_model/where_to_use, so an agent must guess the routing.

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

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

There is no statement of when to use this tool versus alternatives such as best_model_for or where_to_use, and no prerequisites or exclusions. The only hint is the existence of a 'task' concept, which is implied rather than explained.

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?

Annotations already declare readOnly, idempotent, non-destructive, closed-world behavior, so the safety profile is covered. The description adds context the annotations cannot: that the output is an editorial/subjective recommendation rather than a benchmark-derived score, which materially changes how an agent should present the result.

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

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

Two short sentences, zero filler, with the core action front-loaded and the interpretive caveat immediately after it. Nothing could be cut without losing information.

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?

Annotations cover the safety profile and there is no output schema to explain, so the remaining burden is constraint semantics and usage context. Given a nested seven-field constraints object at 0% coverage and four closely related siblings, the definition is thinner than the tool's complexity warrants, though not broken.

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 the only semantic hint is the words "task" and "verified constraints." The nested constraints object has seven enum-valued sub-fields (budget, input, output, style, duration, platform, audio) that are documented nowhere, so the description fails to compensate for the coverage gap.

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 ("Find an AI model") plus the selection axes ("creation task and verified constraints"), so the agent knows it is a filtered lookup rather than a listing. It does not, however, distinguish itself from the very similar sibling best_model_for, so the reader must infer the split.

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

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

There is no explicit when-to-use, when-not-to-use, or alternative named among best_model_for, list_models, or compare_models. The phrase "Editorial recommendation; not a benchmark score" is a disclaimer about result character, not routing guidance, leaving the agent to guess when this beats a sibling.

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?

Annotations already declare readOnlyHint=true, idempotentHint=true, destructiveHint=false and openWorldHint=false, so the safety profile is covered. The description contributes the nature of the payload ('dated citations', 'editorial limitations', 'labeled optional access link'), which hints at curated content, but says nothing about error behavior, lookup failure, or whether the identifier must match exactly.

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 the verb and resource first and no padding. The dense trailing list ('labeled optional access link') is slightly opaque, but nothing is wasted.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description does carry some of the return-value burden by naming the categories of information returned, which is adequate for a simple one-parameter lookup. It nonetheless omits parameter format and any routing guidance relative to find_model/list_models/compare_models.

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% for the single required 'model' parameter, so the schema gives no help on expected format (slug, ID, display name, case sensitivity). The description does not compensate at all, providing zero clarification of what value to supply.

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 ('Get') and resource ('model') and enumerates the returned content (capabilities, dated citations, editorial limitations, access link), which is more concrete than a tautology. However, it never distinguishes itself from close siblings like find_model, list_models, or compare_models, so an agent must infer that this is the fetch-one-by-identifier tool.

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

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

There is no when-to-use guidance, no exclusions, and no mention of any alternative among the seven siblings. The presence of find_model and list_models makes routing ambiguous without any explicit statement that this tool fetches a single known 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, so the safety profile is fully covered elsewhere. The description adds one meaningful behavioral caveat, that the guidance is untested rather than measured, which tells the agent to treat the output as unvalidated. It stops there, adding no detail on source, freshness, or coverage.

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 short sentence that gets the main claim up front. It wastes little space, though the trailing clause about "measured outputs" is the vaguest part and slightly muddies the front-loaded message.

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, 0% parameter coverage, and no usage context, the description leaves too much unspecified for a tool that takes a required model argument. An agent lacks what it needs to know what inputs are valid or what the returned guidance will look like.

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% for two parameters, one of which (model) is required, so the description must compensate and does not. Neither "task" nor "model" is mentioned, and their accepted values or formats are entirely undocumented.

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 pairs a verb ("Get") with a resource ("prompting guidance"), so the general purpose is discernible. However, "untested" is left undefined and the contrast with "measured outputs" is ambiguous, so an agent cannot tell exactly what kind of content it will receive. It also does nothing to distinguish the tool from the model-oriented siblings like best_model_for or compare_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?

There is no when-to-use guidance and no alternative tool is named. The phrase "clearly distinguished from measured outputs" hints at a use case (unverified advice vs. empirical results) but never states when an agent should call this instead of another 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?

Annotations already declare readOnlyHint, idempotentHint and non-destructive, so the safety profile is covered. The description adds a content trait, that results are editorially curated and avoid unmeasured growth/income claims, which is mild but real behavioral context about the payload. It says nothing about result size, pagination, or freshness.

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 with the core purpose front-loaded. The trailing clause about growth/income claims is a slightly odd disclaimer but does not bloat the definition.

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 parameters and no output schema, the description is the only source of information, and it does not explain what a 'spotlight' actually contains or how results are shaped. Adequate for a parameterless discovery call, but thin for an agent trying to decide whether the output is useful.

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

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

The tool takes zero parameters, so there is no parameter semantics to document; the schema is trivially complete. Baseline 4 applies since the description cannot and need not add parameter 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?

It states a verb and a resource ('Get curated workflow spotlights'), and the term 'spotlights' is distinctive enough to separate it from the model-centric siblings. However, 'workflow spotlights' is never defined, so an agent cannot tell whether this returns workflows, marketing blurbs, or templates.

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 when-to-use guidance, no trigger conditions, and no mention of alternatives. Nothing tells the agent when this discovery tool is preferable to, say, list_models or get_prompt_tips.

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?

Annotations already declare this a safe, idempotent, read-only, closed-world operation, lowering the disclosure burden. The description adds a modest content trait (curated, independent non-affiliate options) but says nothing about filtering behavior, result limits, or ordering, so it only partially exploits the available room.

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 no filler, which is efficient. It stops short of 5 because the extreme brevity reads as under-specification for a tool with a nested filter object, rather than optimally concise framing.

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 and a nested filter parameter, the description should at minimum say what the list returns and that filtering is supported. Neither is covered, leaving notable gaps for a filtering/list 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 the single 'filters' object with five nested fields (kind, audio, higgsfield, min_duration, image_to_video) is never mentioned in the description. The schema shape is structured, but the description does nothing to compensate for the total absence of parameter documentation.

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 (List) and resource (curated AI models), with a qualifier about non-affiliate alternatives that hints at the catalog's character. It does not, however, explicitly differentiate itself from retrieval siblings like find_model or get_model, so the agent must infer the distinction.

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 offers no when-to-use guidance, no conditions for preferring this over find_model, best_model_for, or compare_models, and no prerequisites. The agent is left to infer usage from the name alone.

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?

Annotations already cover the safety profile: readOnly, idempotent, non-destructive, and not open-world. The description adds one useful behavioral caveat — results do not guarantee plan entitlements — but does not disclose return format, auth needs, or other operational details.

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, front-loaded sentence with no filler. Every part of it contributes to understanding what the tool returns and one important caveat.

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 an undocumented required parameter and no output schema, the description should clarify the expected model identifier and the shape/meaning of returned access destinations. It gives only a high-level summary and a caveat, leaving key invocation and interpretation details missing.

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 required parameter ('model') with 0% description coverage, and the description does not mention or clarify the parameter at all. It adds no meaning beyond the bare schema field name.

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 ('Return') and resource ('official and optional commercial access destinations'), making clear that the tool surfaces where a model can be accessed. It does not explicitly differentiate itself from model-information siblings like get_model or find_model, but the purpose is still inferable.

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

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

There is no explicit guidance on when to use this tool versus siblings such as best_model_for, find_model, or compare_models. The tool name implies usage, but the description itself provides no when-to-use, prerequisites, or alternatives.

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