AI Model Radar

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

With no annotations provided, the description carries the full burden, and it does well by disclosing the computation basis and the full set of possible verdicts (EXCELLENT through UNKNOWN) with plain-language reasons. As a read-only analysis tool it implies no side effects, and the absence of mutation behavior is reasonably inferable from 'returns' and 'verdicts'.

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, every one earning its place: purpose/first sentence, output detail/second sentence, and prerequisite/third sentence. No filler or repetition of schema fields.

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?

Although there is no output schema, the description compensates by enumerating the verdict values and stating that a best recommendation plus reasons will be returned. Inputs, defaults, and hardware-id sourcing are all covered via schema and description, making this complete for an agent to select and invoke 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 description coverage is 100%, so the schema already documents each parameter clearly. The description adds extra meaning by linking the context and kv parameters to the measured/computed analysis, and by telling the agent where hardware ids come from (list_hardware), which is beyond what the schema states.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a clear, specific question—'Which tracked models run on a given GPU or Mac'—and goes on to name the exact analysis (GGUF weights, context cache, runtime overhead vs. usable memory). This clearly differentiates find_fit from siblings like gpu_prices, list_hardware, and trending_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 description makes the core use case obvious: when a user has a target GPU or Mac and wants to know which models fit. It also gives a concrete prerequisite by pointing to list_hardware for obtaining hardware ids, but it does not explicitly state when to avoid this tool or prefer gpu_prices/trending_models.

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 present, so the description carries the full burden of behavioral disclosure. It transparently describes the returned data: median price, min/p75, offer counts, and collection timestamp. It does not elaborate on staleness or update semantics, but the presence of the timestamp partly addresses freshness and the operation is clearly a read-only query.

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 delivers the essential information with no wasted words. It names the metric, scope, source, unit, statistics, and timestamp efficiently.

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 zero-parameter tool with no output schema, the description is sufficiently complete. It tells the agent what data will come back, the unit of measure, the source, and the time reference, leaving no critical gaps for invoking the tool correctly.

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

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

The tool has zero parameters, so parameter documentation is not needed. The description instead clarifies the output dimensions, which is appropriate for a no-input data lookup tool.

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 identifies the tool's resource (GPU rental prices), the specific metric (median verified on-demand price per GPU class), unit (USD/hour), and source (Vast.ai). It lacks an explicit imperative verb like 'Returns' or 'Lists,' but the meaning is unambiguous and distinct from the sibling 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 provides no guidance about when to choose this tool over siblings such as find_fit, list_hardware, or trending_models. There are no stated use cases, exclusions, or alternatives, so the agent must infer appropriateness from the title and content 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?

With no annotations, the description carries full burden. It discloses that profiles are 'curated', scoped to what the fit engine knows, and lists the exact data dimensions returned, including usable memory after margins. For a zero-parameter list operation, this adequately sets expectations without hiding important 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?

The description is two short sentences with no filler. It front-loads the resource and key fields, then adds the actionable follow-up instruction. Every clause contributes useful 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?

For a simple zero-parameter list tool with no output schema, the description covers the scope (GPU/Mac), the returned fields, and the intended usage with find_fit. It omits formatting and pagination details, but those are not essential for this operation.

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

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

The tool has zero parameters, so the schema is trivially complete and the description has nothing to add at the parameter level. The baseline of 4 for zero-parameter tools 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?

The description clearly states that the tool provides curated GPU and Mac profiles known to the fit engine, with fields like ids, memory, usable memory, and bandwidth. The verb is implied by the title and the resource is unambiguous, but it does not explicitly differentiate from sibling tools such as gpu_prices or trending_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 description gives a clear follow-up instruction: 'Use an id with find_fit', which tells the agent what to do after listing. However, it does not explicitly state when to prefer this tool over the alternatives or provide exclusions, leaving some usage context to inference.

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 behavioral disclosure burden. It explains the Heat Score calculation basis, the meaning of heat=null for models with less than a week of history, and that such models rank after scored ones. This goes well beyond a minimal description, though it does not cover response format details or side effects.

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 information-dense but efficient, with the main point front-loaded and the null-ranking edge case in a second sentence. The first sentence is somewhat heavy with parentheticals, but every phrase earns its place.

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

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

Given the simplicity of the tool and the absence of an output schema, the description covers the important return concepts: ranking metric, raw signals, local-run facts, links, and null-heat handling. It does not spell out the exact response shape or sort direction, but for a low-complexity list-like tool this is reasonably 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?

Schema description coverage is 100%, so the baseline is 3. The description adds no parameter-specific detail beyond the schema; both slug and limit are already well documented in the input schema, so no extra semantic guidance is needed.

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 specific resource (tracked AI models) and the key behavior (ranked by Heat Score), clearly distinguishing this from the hardware/GPU-focused siblings like gpu_prices and list_hardware. Even though no explicit sibling comparison is made, the domain and content are 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?

Usage is implied: an agent would call this when it needs trending model rankings or model comparison data. However, there is no explicit statement about when to choose this tool over find_fit, gpu_prices, or list_hardware, and no exclusions or alternative routing guidance.

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