Agent Cost Hub

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

The annotations already define the tool as safe (`readOnlyHint: true`) and constrained (`openWorldHint: false`). The description provides no additional critical behavioral constraints (like regional pricing differences or latency implications), making a baseline score appropriate.

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 perfectly sized and front-loaded. It explains the core function and the input logic in a single, efficient sentence, avoiding unnecessary fluff.

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?

Despite having 8 highly complex parameters, the description successfully provides a high-level functional context. It guides the AI on how to simplify the complexity via presets, ensuring the agent understands the tool's practical utility in matching cost to workload.

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?

Given that the input schema coverage is 100%, the schema itself fully documents all 8 parameters. The description's added value is low, merely framing the purpose of the numerical inputs rather than adding technical meaning 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 core action ('Rank models by monthly cost') and the target ('a workload'). It successfully distinguishes this function from cost estimation or simple model listings by framing the output as a ranking based on workload parameters.

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 immediate and actionable guidance by offering two distinct paths: using a provided workload preset (chatbot, rag, etc.) or manually supplying granular token numbers. This effectively tells the AI how to decide which parameters to populate.

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 and openWorldHint=false, so the safety profile is covered. The description adds that comparison is purely on price and optional monthly cost, but says nothing about return format or how per-model results are structured. With annotations carrying safety, a 3 is fair.

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 waste; the model-count constraint and optional dimension are stated immediately.

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 7-param read-only tool with 86% schema coverage, the description conveys the essential behavior. The main gap is that with no output schema, it doesn't sketch what a comparison result looks like, but overall 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?

Schema coverage is 86%, so the schema itself documents nearly all parameters including presets and token defaults. The description adds only the 2-5 bound and the optionality of workload cost, which is largely redundant with the schema's minItems/maxItems and preset description.

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 (compare) and resources (price, monthly workload cost) with a clear 2-5 model scope. This distinguishes it from single-model siblings like get_model_price, though it doesn't name alternatives explicitly.

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 'optionally' clarifies that a workload cost estimate is conditional on providing workload params, but it doesn't state when to prefer this over estimate_cost or cheapest_models_for. Usage is implied rather than guided.

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 and openWorldHint=false, so safety is covered; the description adds genuine behavioral detail: presets fill in token defaults, explicit numbers override them, and cached_percent/steps_per_task have environment-implied defaults. It stops short of saying what the response contains (monthly total only? breakdown?) despite there being no output 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?

Two sentences, front-loaded with the outcome and then the 'Easiest:' shortcut. Every clause carries information — no restatement of the title or filler.

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

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

For a 7-parameter read-only calculator with a fully documented schema and no output schema, the description is nearly sufficient. The gap is the shape of the result: it never says whether the estimate is a single monthly figure, includes per-call breakdown, or what currency/units are used.

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

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

Schema coverage is 100%, so every parameter is already documented, including the default logic for cached_percent and steps_per_task. The description reinforces the preset-versus-explicit-number precedence, which is a useful relationship the schema only states per-field, but it adds little beyond that baseline.

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 specific verb and resource: 'Estimate the monthly API cost of an AI agent or chatbot on one model.' The 'on one model' phrasing implicitly separates it from compare_models, but no sibling is named explicitly, so differentiation rests on inference.

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

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

It gives two concrete usage paths — 'Easiest: pass model + preset' versus supplying input_tokens, output_tokens and calls_per_day — and states the override rule. What's missing is guidance on which path to prefer when both are plausible, or any mention of when a sibling like get_model_price would be the better call.

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 and openWorldHint=false, so the safety profile is covered elsewhere. The description adds genuine value by disclosing the shape of the return (three price components at a per-1M-token basis plus illustrative monthly agent costs), but it omits currency, data freshness, and what happens when the model id is unknown — all relevant for a pricing lookup whose numbers may drive decisions.

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?

One sentence, front-loaded with the core purpose and followed by the return contents. No filler, no repetition of the title or annotations — every clause carries 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 one-parameter read-only lookup with no output schema, the description is nearly sufficient: it tells the agent which price dimensions come back and in what unit. The remaining gap is currency and whether 'example monthly agent costs' are assumptions the caller should interpret cautiously, which matters given the illustrative framing.

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 is a single parameter with 100% schema description coverage, and the schema already explains it accepts a model id like 'anthropic/claude-sonnet-5.5' or a name. The description adds nothing beyond that, so the baseline of 3 applies; no ambiguity is introduced, but no extra matching or fallback behavior is clarified.

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 ('Current API list price of one model') and enumerates what is returned: input, output, and cached input pricing per 1M tokens plus example monthly agent costs. It scopes to 'one model', which implicitly separates it from multi-model siblings, but it never names compare_models, cheapest_models_for, or estimate_cost, so sibling differentiation is left to inference.

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

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

Usage is only implied by the phrase 'one model' — an agent can guess this is the single-model lookup versus list_models or cheapest_models_for, but no when-to-use or when-not-to-use rule is stated. There is no guidance on choosing this over estimate_cost when the goal is cost projection rather than raw price lookup.

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 and openWorldHint=false, so the safety profile is covered. The description adds the pricing unit (per 1M tokens), which is meaningful, but says nothing about ordering, pagination, or how large the returned catalog is — relevant for a list tool with no output 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?

Two tight sentences with the resource and the return payload front-loaded and filtering deferred to the end. No filler and nothing redundant with the schema.

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

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

With no output schema, the description carries the burden of describing returns, and it does name the list plus the price field. It still omits what a model record contains beyond price, the ordering of results, and the meaning of popular_only, leaving an agent to discover these by calling.

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 33%: provider has an inline example, while search and popular_only are bare. The description recovers provider and search ('filter by provider or text') but leaves popular_only completely undefined, so the compensation is partial.

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 (LLMs tracked by Agent Cost Hub) and adds what comes back — API list prices per 1M tokens. That is enough to separate it from single-model lookups like get_model_price, but it never explicitly contrasts with cheapest_models_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?

'Optional filter by provider or text' implies the unfiltered call returns the full catalog, which is useful context. However, there is no guidance on when to choose this over cheapest_models_for or compare_models, nor any mention of when a filtered list is preferable to a targeted lookup.

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