AIGratis

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

Annotations already declare readOnly, idempotent, and non-destructive behavior. The description adds valuable behavioral detail beyond annotations: the sort order, the exact fields returned, the limit/totalOffers semantics, and the fallback-to-search behavior. This gives the agent a clear picture of what will happen when invoked.

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 three sentences, each earning its place: the first states the purpose and sort order, the second lists output fields, and the third explains the limit and fallback behavior. It is front-loaded with the core purpose and contains no redundant text.

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 tool has no output schema, the description adequately explains the return values, ordering, included fields, and fallback behavior. The parameter semantics are covered by the schema and enriched by the description, and the annotations handle safety expectations. Nothing essential is missing for an agent to invoke this 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?

Schema coverage is 100%, so the baseline is 3. The description adds meaning beyond the schema by explaining that `model` can be a canonical slug or partial name and triggers a fallback search, and that `limit` determines the number of cheapest offers returned alongside totalOffers. This enriches the parameter understanding.

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 action ('compare model prices') and resource ('every gateway offering one canonical model') and clearly specifies the output: cheapest input price first, with context length, cached-input price, isFree, referral link, last synced, and totalOffers. It also distinguishes itself by describing a fallback to search when the exact slug is unknown, which clarifies its unique role among siblings.

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

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

The description implies usage: call this tool to get the cheapest offers for a canonical model, and it notes that a partial slug triggers a search. However, it does not explicitly state when to prefer this tool over siblings like search_models, estimate_cost, or list_gateways, nor does it provide explicit exclusions.

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 establish that the operation is read-only and idempotentщее. The description adds useful behavioral detail beyond that: results are limited to the cheapest gateways, include both USD and IDR, provide a total count and a cheapest entry, and treat free offers as costing 0. It does not contradict the annotations.

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 with no filler: the first sentence states the core purpose, and the second concisely enumerates the return fields and a key edge case. Each clause earns its place, and the most important information is front-loaded.

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 that this is a read-only calculation tool with four fully documented parameters and no nested objects, the description is largely complete: it explains the calculation, the required token inputs, the output fields, and the free-offer behavior. It could slightly expand on empty results or error cases, but no output schema exists and the description already covers the essential response shape.

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%; every parameter already has a description in the schema, including defaults, bounds, and the model source. The description only paraphrases that input and output tokens are expected and does not add semantic detail beyond the schema. A baseline of 3 is therefore appropriate.

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 operation: computing a monthly bill for a model across gateways based on input/output tokens, and names the returned artifacts (cheapest gateways, totalOffers, cheapest entry). It is specific enough to identify the tool's purpose, though it does not explicitly contrast itself with the sibling `compare_model_prices`, leaving some differentiation to the name and title.

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 'given expected input and output tokens' communicates the main input scenario)Skip. No explicit guidance is given about when to prefer this tool over `compare_model_prices`, `top_free_gateways`, or `list_gateways`, and there are no exclusions. The usage context is implied rather than explicitly 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering safety. The description adds useful context by explaining what perUsd means. It does not disclose which currencies are included or response format, but these are minor given the tool's simplicity.

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 no filler and places the purpose first, followed by the precise unit definition. Every word contributes to the agent's understanding.

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 parameterless, read-only, idempotent tool, the description is nearly complete: it explains the tool's purpose and rate semantics, while annotations cover safety. It omits details like currency coverage or staleness, but these are not essential for correct invocation.

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 zero parameters, the schema is empty and the baseline is 4. The description adds meaning by explaining that the rates are used to convert USD prices, which is the only context needed for a parameterless 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 as providing exchange rates for converting USD prices, and it defines the unit semantics (perUsd = units of currency per 1 USD). This makes the resource unambiguous. It does not explicitly differentiate from siblings, but the domain is distinct enough from the model/price-related siblings.

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

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

The phrase 'used to convert USD prices' provides a clear context for when this tool should be invoked. It does not mention alternatives or exclusions, but for a parameterless lookup tool the use case is sufficiently specified.

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 establish this as a safe, read-only, idempotent operation, so the description does not need to cover safety. It adds useful behavioral context beyond annotations by specifying that the response includes a full model list sorted by input price, which helps the agent understand the tool's output characteristics.

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, tightly written sentence with no filler. It front-loads the core purpose ('One gateway's profile') and packs the key return details and ordering behavior into an efficient 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 one-parameter, read-only tool with strong annotations, the description covers the essential information: what the tool returns, what details are included, and the ordering. Nothing critical is missing for an agent to select and invoke this 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 schema describes the slug parameter as coming from list_gateways, and the description does not add much additional parameter meaning beyond that. With 100% schema description coverage, the schema carries the semantic weight, so a baseline of 3 is appropriate.

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 and resource: retrieving one gateway's profile plus its full model list, with details like prices, context length, modalities, and isFree. This clearly distinguishes get_gateway from list_gateways, which presumably lists gateways without full model details.

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 the primary use case: fetching a single gateway's detailed information by slug. It also points the agent to list_gateways as the source of the slug, giving a clear usage context. However, it does not explicitly name alternatives or state when not to use this 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 cover read-only, idempotent, non-destructive behavior. The description adds meaningful context: only active gateways are returned, the output is compact with a defined field set, and editor's picks are ordered first by default.

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 with no filler; the core action, returned fields, default ordering, and sibling pointer are all front-loaded and compact.

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 read-only list tool, the description covers scope, returned fields, default ordering, and where to get more detail. Pagination is inferable from schema defaults, and annotations cover side-effect safety.

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 33%, covering just 'sort'. The description clarifies the default sort order but does not explain limit or offset semantics, leaving the agent to infer pagination behavior from names and bounds.

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: lists active AI gateways in compact form. Names the included fields and explicitly distinguishes itself from get_gateway, which is the detailed variant.

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?

Explicitly tells the agent when to use get_gateway instead (for docs, payment methods, notes, and model list). This provides a clear routing rule among siblings.

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 idempotentHint=true, covering safety. The description adds behavioral context by enumerating the event types and stating the ordering and pagination behavior. It does not describe the return structure or error handling, but the annotations reduce the burden, so this is sufficient.

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 three short sentences, front-loading the core purpose and then adding filtering and pagination. Every sentence earns its place with no fluff. It is concise and 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 covers the essential aspects: event types, ordering, filtering, and pagination. It does not describe the structure of the returned events, but no output schema exists, so some ambiguity remains. Given the tool's simplicity and annotations, this is reasonably complete, though it could mention what fields an event contains.

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 60%, with descriptions for model, cursor, and provider. The description adds meaning to 'kind' by listing the event types that correspond to the enum, and confirms filtering for provider and model. However, it doesn't add much beyond the schema for limit, which is standard. Overall it provides marginal value over 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 it lists catalog events with specific types (added, removed, returned, price changes, free/paid, new gateways) and orders them newest first. This distinguishes it from sibling tools like compare_model_prices or search_models, which serve different purposes. The verb 'list' and resource 'catalog events' are specific.

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 operational guidance on filtering by gateway or model and paginating with a cursor, but does not explicitly state when to choose this tool over siblings. The purpose implies it for browsing recent changes, but no exclusions or alternative conditions are given. This is adequate but not explicit.

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 covered. The description adds genuine value beyond that: per-row semantics (model×gateway rows), price units, the free filter behavior, and the canonicalSlug output contract. It doesn't describe pagination behavior or result counts, but for a read-only search this is solid supplementary context.

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?

Four short sentences, each earning its place: purpose, row semantics and units, filter behavior, and return contract with downstream routing. No filler, no repetition of schema 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?

Despite no output schema and 10 optional parameters, the description covers the essentials an agent needs: what is searched, result granularity, price units, the most important filter, and the key return field (canonicalSlug) plus how to use it downstream. The only gap is that pagination and sorting behavior are never acknowledged, leaving those inferred purely from parameter names.

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 70%, below the high-coverage baseline, with page, sort, and perPage lacking schema descriptions. The description doesn't compensate for those, though they are largely inferable from names, defaults and enum values. It does reiterates the free filter and adds that canonicalSlug is the useful output field, providing modest added 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?

States a specific verb+resource ('Search model offers across all gateways') and, crucially, defines the unit of each result ('one model at one gateway with its input/output price in USD per 1M tokens'). This row granularity plus the downstream-tool reference clearly sets it apart from siblings like list_gateways, suggest_models, and compare_model_prices.

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 clear usage context: search_models is the discovery step that 'Returns canonicalSlug for use with compare_model_prices and estimate_cost,' establishing the intended workflow. It also advertises the free=true shortcut. It does not explicitly state when NOT to use it versus siblings (e.g., list_gateways), so it stops short of a 5.

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 destructiveHint, so the description is not burdened with stating safety. It adds useful behavioral detail: matching returns up to `limit` canonical models and includes gateway counts, free availability, and cost previews based on token 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?

Two sentences with no filler. The primary action is front-loaded, return-value highlights are compact, and the routing instruction to compare_model_prices is placed at the end without bloating the description.

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 read-only, idempotent lookup tool with no output schema, the description adequately covers what the agent gets back and how parameters affect results. Minor omissions like ordering or exact slug format are not critical given the explicit return summary.

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 75%, and the description compensates well by explaining `limit` semantics ('Returns up to `limit` canonical models') and tying `inputTokens`/`outputTokens` to the 'cheapest monthly bill.' This adds value beyond the schema for the parameter that lacked a 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?

The description states a specific action ('resolve a loose model name to canonical slugs') and clearly differentiates the tool from siblings like search_models and compare_model_prices. The mention of returning limit, gateway counts, free counts, and cheapest bill makes its purpose concrete.

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 says to use this tool 'before compare_model_prices when you only have a rough name,' giving a clear usage scenario and a named alternative. It does not exhaustively rule out other siblings like search_models or estimate_cost, 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?

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false, covering the safety profile. The description adds the ranking criterion and example model detail, which is useful, but it does not disclose other behavioral aspects like pagination or output format. It adds modest value beyond annotations without contradicting them.

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 wasted words. It states the core ranking logic and the inclusion of example models efficiently, making it easy to parse quickly.

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 read-only tool with one optional parameter, the description gives the primary purpose and hints at the return content (example models). However, it omits any mention of the 'limit' parameter's role or the output structure, and with no output schema, an agent might be uncertain about the exact response shape. The core functionality is clear, but completeness is lacking for a fully self-contained description.

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 'limit' parameter, and the description does not mention it at all. The agent must infer that 'limit' controls the number of returned gateways, which is plausible but not explicitly stated. The description fails to compensate for the schema gap, providing no direct guidance on the parameter's 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 description states a specific verb ('ranked') and resource ('gateways'), and clearly differentiates from siblings like compare_model_prices or list_gateways by specifying the ranking criterion (number of distinct free models) and the inclusion of example models. This is sufficient for an agent to identify the tool's 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 implies usage when one wants gateways ranked by free model count, but it does not explicitly state when to use this tool versus alternatives like list_gateways or suggest_models. There is no exclusion or mention of alternative tools, leaving the routing decision to inference.

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