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

With no annotations, the description carries the disclosure burden. It usefully reveals the output ordering ('cheapest first') and that provider/tier are optional filters, but says nothing about pagination, which models are excluded, or that results are pure computation with no 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?

Two short sentences, zero filler, with the comparative scope and output ordering front-loaded before the usage recommendation. Every clause 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?

There is no output schema and eight parameters, so the description should say more about the result: whether it returns one row per model, what cost fields appear, or how models without a listed price are handled. It is adequate for a read-only price comparison but leaves the agent guessing at the response shape and the relationship to estimate_cost.

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%, so most parameters are already documented. The description only reinforces the optional provider/tier filtering and the 'same request' premise applied uniformly across models; it adds no meaning for date semantics, batch behavior, or cached_input_tokens beyond what the schema states. Baseline 3 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?

States a specific verb and scope: 'Price the same request on every model', with optional provider/tier narrowing and 'cheapest first' output ordering. This implicitly separates it from single-model siblings like get_model_price, but no sibling is named explicitly, so the differentiation is inferential.

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?

Gives clear when-to-use guidance: 'Use to choose a model on cost before testing quality', which frames the decision stage this tool serves. It does not say when to prefer estimate_cost or estimate_text_cost instead, so the boundary with the cost-estimation siblings is left 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 full burden and does disclose useful behavior: the result is in USD, includes an input/output breakdown, and factors in caching, batch discounts, long-context surcharges, and announced price changes. It does not state that the tool is a pure read-only calculation, nor describe the shape of the returned breakdown, which matters since there is 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?

A single dense sentence with the core output (USD cost) front-loaded and the supporting pricing dimensions trailing. Every clause maps to a real feature of the tool; it is efficient, though the enumeration is packed tightly enough to read as a feature list rather than guidance.

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 pricing calculator with no annotations and no output schema, the description covers the pricing dimensions well but leaves the return value layout, rounding/currency assumptions, and behavior for unknown models or dates unaddressed. Adequate, but an agent must still probe the output to understand the breakdown.

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 already defines date, batch, requests, token counts, and cached_input_tokens in detail. The description adds only the high-level framing of which pricing dimensions are modeled (batch, caching, long context) and does not explain parameter interactions such as cached_input_tokens being a subset of input_tokens beyond what the schema says.

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 computation (total USD cost of N requests on one model) and enumerates the modifiers it accounts for (caching, batch, long-context surcharges, announced price changes). This clearly separates it from siblings like get_model_price or list_models, though it never names those siblings 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?

Usage is only implied: 'cost of N requests on one model' suggests this is the tool for multi-request totals rather than per-token pricing. There is no explicit statement of when to prefer this over get_model_price, compare_models, or estimate_text_cost, and no exclusions or prerequisites.

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, and it does disclose the estimation methodology (measured tokens-per-character for the language) and an automatic script-detection fallback when language is missing. It never confirms this is a side-effect-free calculation, offers no accuracy/precision caveat, and doesn't state what the response contains.

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, front-loaded with the core action and followed by the one behavioral caveat; nothing is restated from the schema or 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?

For a five-parameter tool with no annotations and no output schema, the definition covers intent and most parameter meaning but never describes the return shape (token count, cost, per-unit breakdown), which an agent must know since nothing structured supplies it. Adequate but with a clear 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?

Schema coverage is only 20% (just `messages` is documented), so the description has to compensate and largely does: it explains `as` as input vs output pricing, `messages` as N copies of the same text, `language` as optional with auto-detection, and `model` as the target for tokenization. The only gap is any expected format for `model` or `language` identifiers.

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 (estimate) plus the exact quantities produced (tokens and cost) and the scope ('a text ... on a model'). The phrase 'a text' implicitly differentiates it from estimate_cost, but no sibling is named, so an agent must infer the boundary from the descriptions of the others.

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 explicit when-to-use or when-not-to-use guidance, and no signposting toward estimate_cost, get_model_price, or compare_models. The only conditional behavior mentioned ('Detects the script if language is omitted') is a runtime action, not guidance on selecting 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?

With no annotations, the description bears the full burden; 'full pricing for one model' implies a read-only lookup, but nothing states that it does not mutate anything, how an unknown or non-existent model ID is handled, or how fresh the prices are. It does add real value by listing exactly which price categories come back, which substitutes for the missing 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?

A single front-loaded sentence with no filler; the itemized list is dense but each element conveys a distinct returned price category. Slight cost in readability from packing seven items into one clause.

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 lookup with no output schema, the description does the main job of telling the agent what data comes back. It omits units/currency, per-token vs per-million normalization, and error behavior, which matter for a pricing tool an agent will feed into cost calculations.

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?

Only one parameter exists and schema coverage is 100%, with the schema itself giving format examples ('gpt-6.1-terra or Claude Sonnet 5.5'). The description adds no matching or normalization rules beyond the schema, so baseline 3 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?

Names a specific resource (full pricing) with a clear scope restriction (one model) and enumerates the price dimensions returned, so an agent can distinguish it from list_models and compare_models without opening the schema. It never names a sibling explicitly, which keeps it just short of a 5.

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 or when-not-to-use guidance and no mention of alternatives such as estimate_cost (which computes a spend figure) or compare_models (multi-model). That the tool is for a single model's detail is only inferable from the phrasing 'one 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?

No annotations exist, so the description carries the full burden; it does disclose the substantive output contract (three price categories, USD, per-million-token normalization) which is the key behavioral detail. It stops short of stating read-only nature explicitly, data freshness, or whether the result is a static snapshot.

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 well-formed sentence with no filler. The resource and provider scope are front-loaded and the price detail follows, so scanning yields the essentials 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 simple two-filter read tool with no output schema and no annotations, the description covers what is returned well enough to invoke correctly. The remaining gap is the unexplained 'tier' filter, which an agent would have to inspect the schema for and would still not fully understand.

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 partially covers the provider filter by naming the OpenAI/Anthropic/Google enum values, but the 'tier' parameter (flagship/mid/small) is never mentioned or explained, leaving half the filtering surface undocumented anywhere.

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 ('List') and a concrete resource ('current OpenAI, Anthropic and Google API models'), then enumerates the price dimensions returned (standard, cached-input, batch) in USD per million tokens. This makes the catalog-listing nature unambiguous versus siblings like get_model_price or estimate_cost, though no sibling is named directly.

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: an agent can infer this is the browse/discovery tool for the model catalog. There is no explicit statement of when to prefer this over get_model_price, compare_models, or estimate_cost, and no prerequisites or exclusions are given.

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 at all, the description carries the full behavioral burden and only delivers ordering and record shape ('dated, sourced', newest first). It says nothing about read-only semantics, whether history is complete or bounded, what providers/time window the log covers, or what happens when limit is smaller than the available history.

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 tight sentence with the key qualifiers front-loaded and no filler. Its brevity is a virtue structurally, though it borders on under-specification rather than pure efficiency.

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-optional-param, read-only listing tool with no output schema, this is close to adequate: it conveys that results are chronological and carry dates and sources. It still leaves out the scope of coverage (which providers, how far back) and how limit interacts with the log depth, which an agent would want before calling it.

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 optional `limit` parameter, and the description never explains what limit controls, its default of 10, or the cap of 50. '(newest first)' implies ordering but does not tell the agent that limit truncates the newest-N entries.

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 the resource precisely — a dated, sourced log of LLM API price changes and pricing announcements — which is clearly distinct from pricing siblings like get_model_price (single current price) or estimate_cost. The verb is only implicit ('log of'), so it never explicitly says 'list' or 'returns', but the resource is 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?

There is no when-to-use guidance and no sibling is referenced, even though the toolset contains several pricing tools (get_model_price, compare_models, token_tax) that an agent could easily confuse with this one. The parenthetical '(newest first)' hints at a recency-oriented use case, but the agent must infer 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?

With no annotations, the description carries the full burden; it implies a read-only lookup by framing the output as a per-model measurement and enumerating the covered models, which reassures the agent nothing is mutated. It does not say whether the values are static measurements or live computations, nor anything about return shape (ratios, percentages, table structure).

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 compact clauses, with the metric definition front-loaded and followed by scope and the omission rule; nothing is padded. Naming four model families costs some space and partly duplicates list_models, but it usefully fixes which models the numbers apply to.

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 single-parameter lookup with no output schema, the description covers the metric, model coverage, language coverage, and no-filter behavior. What is missing is the return shape — whether each model yields one ratio, a percentage, or a richer record — which an agent may need to interpret results since no output schema exists.

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% and the schema already documents name-or-code examples, so the baseline is 3. The description adds the default behavior when the parameter is omitted ('list all'), which the schema does not express despite the parameter being optional — genuine 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 states a precise, unusual metric — how many more tokens the same text takes in a language than in English — plus the measured models and language count. This is far more than a restatement of the name, though it never contrasts itself with siblings like estimate_text_cost or compare_models, which an agent might otherwise reach for.

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 one concrete usage rule: 'Omit language to list all,' which tells the agent how to use the optional parameter as a discovery mechanism. It offers no guidance on when to prefer this over estimate_text_cost or compare_models, so selection among siblings is left to inference.

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