AI Pricing Hub

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

Annotations indicate readOnlyHint=true, which is consistent with the description implying a read operation. However, beyond that, the description discloses only filtering capabilities but lacks details on pagination behavior, data source, or any side effects. Given the annotation coverage, the description adds some but not rich behavioral 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?

The description is a single concise sentence that captures the essential purpose with no wasted words. It is efficiently front-loaded and focused.

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 presence of 5 parameters including pagination (limit, cursor) and an output schema, the description is minimal. It does not explain how pagination works, what constitutes a 'sourced' benchmark row, or provide enough detail for an agent to correctly compose queries without additional context.

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% but parameter descriptions are minimal (e.g., 'limit', 'cursor'). The description adds value by specifying that the tool can filter by model, provider, or benchmark ID, but doesn't explain the role of limit and cursor or provide any additional semantics 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 tool finds benchmark rows using a specific verb 'Find' and resource 'benchmark rows'. It mentions filtering by model, provider, or benchmark ID, which distinguishes it from sibling tools like 'compare_models' or 'search_models' that have different purposes.

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 on when to use this tool versus alternatives. There is no mention of when not to use it, prerequisites, or comparison with sibling tools like 'search_models' or 'latest_changes'.

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?

The description 'Estimate cost' aligns with the readOnlyHint annotation, indicating a safe read operation. No additional behavioral traits are disclosed beyond what annotations provide, but there is no contradiction. With annotations covering the read-only nature, a baseline score of 3 is 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 a single concise sentence with no waste. It is front-loaded with the main purpose. However, it could benefit from slightly more detail without becoming verbose.

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 5 optional parameters and an output schema, the description is too minimal. It does not explain how the cost is calculated (e.g., formula, whether it sums tokens or calculates per-request) or what the return format is. The output schema may cover return values, but the description itself leaves significant gaps.

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%, but parameter descriptions are minimal (just parameter names). The description adds no extra meaning beyond the schema, so a baseline score of 3 is correct.

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 estimates cost from model pricing and token volumes, which is a specific verb+resource combination. It distinguishes from siblings like 'pricing_history' or 'find_cheapest' by focusing on calculation, though it could be more precise (e.g., total cost or per-request).

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 guidance is provided on when to use this tool versus alternatives like 'find_cheapest' or 'compare_models'. There is no mention of prerequisites, edge cases, or typical scenarios.

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, so the description does not need to reiterate read-only behavior. It adds no further behavioral traits (e.g., limits on model count, data freshness), thus providing minimal extra value beyond 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 a single concise sentence that front-loads the core purpose. It is efficient, though it could benefit from slight restructuring for clarity (e.g., listing comparison axes).

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 an output schema, the description omits crucial details about what 'context' and 'sourced benchmarks' entail. For a comparison tool, users need to know the output format and scope, making the description incomplete.

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% with the parameter description 'model ids'. The tool description rephrases this without adding new semantic details (e.g., accepted formats, number of IDs). Given high schema coverage, baseline 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 clearly states the action 'compare' and the objects 'pricing, context, and sourced benchmarks for model IDs', distinguishing it from sibling tools like benchmark_lookup or calculate_cost. However, 'model IDs' is slightly ambiguous as it may imply comparing the IDs themselves rather than the models they represent.

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 guidance on when to use this tool versus alternatives like recommend_model or find_best_value. The description lacks context for selection, making it hard for an agent to decide without additional information.

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 declare readOnlyHint=true, so the description is not required to restate safety. It adds the behavioral detail of ranking by a value metric but does not disclose pagination behavior, data freshness, or how 'available benchmark signal' is computed.

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, concise sentence (10 words) with no filler. It is front-loaded with the verb 'Rank' and efficiently conveys the tool's core functionality.

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 is too brief given the tool has 4 optional parameters and a ranking operation. It lacks explanation of how the ranking is performed or how parameters influence results. The presence of an output schema partially compensates for missing return value details.

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?

Although schema description coverage is 100%, all parameter descriptions are minimal (e.g., 'provider', 'benchmark id'). The tool description provides high-level context but does not explain individual parameter usage, meaning, or valid values.

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 ranks models by benchmark signal per token price, directly specifying the verb, resource, and criterion. This distinguishes it from sibling tools like find_cheapest (price-only) and compare_models (general comparison).

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 guidance is provided on when to use this tool versus alternatives. There is no mention of use cases, prerequisites, or exclusions, leaving the agent to infer context from the tool name and description 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 declare readOnlyHint=true, so the tool is read-only. The description adds that price is combined input+output token cost, but does not disclose other traits like sorting order or grouping. With annotations covering safety, a score of 3 is 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?

A single sentence that is concise and straight to the point, but could be slightly more structured with bullet points or examples for brevity and clarity.

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 an output schema, the description is too brief for a tool with 4 parameters. It does not explain what 'cheapest' means in case of ties, how limit works, or how to use cursor for pagination. More detail is needed.

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% but each parameter description is just the parameter name (e.g., 'limit', 'cursor'), providing no meaningful information. The tool description does not mention or explain parameters, so it adds no value 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 tool finds cheapest models by combined input+output token price, which is a specific verb+resource. It distinguishes from siblings like find_best_value and calculate_cost by focusing on price-based ordering.

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 when to use (when seeking cheapest models by price), but lacks explicit guidance on when not to use or comparisons to alternatives. No exclusions or context for sibling tools.

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 the description is consistent ('return'). No additional behavioral details (e.g., pagination, rate limits) are added beyond what annotations provide.

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 wasted words. It could benefit from brief structure (e.g., listing parameter purposes) but is concise.

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?

Output schema exists but is not described; given the tool's simplicity, the description is adequate. However, parameter meanings are unclear, which reduces completeness for an agent.

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?

Input schema has 100% coverage but each parameter description is just its name (e.g., 'type', 'limit'). The description does not add meaning or usage context beyond the minimal schema 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 clearly states it returns recent model launches, removals, and pricing changes. It uses a specific verb ('return') and resource ('changes'), distinguishing it from sibling tools like search_models or pricing_history.

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?

Implied usage is to get recent updates, but no explicit when-to-use or when-not-to-use guidance is provided. No alternatives are mentioned despite having many sibling tools.

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?

The description aligns with the readOnlyHint annotation by stating 'Return,' indicating a safe read operation. However, it does not disclose additional behavioral traits like data range, granularity, or error handling. The annotation already covers safety, so the description adds limited 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?

The description is a single, front-loaded sentence with no wasted words. It is efficient, though slightly terse.

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 (one parameter, read-only, output schema exists), the description is mostly adequate. It could mention that model_id accepts multiple types or that it returns an array, but the schema covers that.

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%—the only parameter has a schema description 'model id.' The tool description adds no further meaning beyond what the schema provides, so baseline 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 clearly states the action (Return), resource (historical pricing snapshots), and target (model ID). It distinguishes the tool from siblings like latest_changes or calculate_cost by focusing on historical pricing data.

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 on when to use this tool versus alternatives such as latest_changes (presumably for recent changes) or benchmark_lookup. No exclusions or context 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?

Annotations already declare readOnlyHint=true, so description adds value by specifying what is summarized (count, prices, benchmarks, changes). No contradiction.

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?

Single sentence, no filler words. Every part is necessary and informative.

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 one optional parameter and an output schema, the description adequately covers what the tool does (summarize multiple aspects). Could be improved by noting that output is structured, but output schema handles that.

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% for the single parameter 'provider', but both schema and description provide minimal detail. Description adds context that summary is 'for a provider', which is slightly more than schema's bare 'provider'. Baseline 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?

Description uses specific verb 'summarize' and resource 'provider', and lists specific aspects (model count, prices, benchmarks, changes). Clearly distinguishes from siblings like compare_models or search_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?

No guidance on when to use this tool vs alternatives like latest_changes or calculate_cost. No exclusion criteria or prerequisites mentioned.

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 indicate readOnlyHint=true, and the description is consistent. The description adds that it uses price, context, and benchmark evidence, which is slightly beyond the annotation but does not provide deep behavioral insight.

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, no wasted words. Front-loaded with the core purpose.

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 an output schema present and 100% schema coverage, the description is somewhat complete but could be improved by explaining how recommendations are generated and what the output 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 100%, but param descriptions are just names (e.g., 'limit'). The description adds some context by mentioning workload, price (budget), and context (min_context_tokens), but does not fully explain all parameters like provider.

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 tool's purpose: recommending models based on workload, price, context, and benchmarks. It distinguishes from siblings like benchmark_lookup and calculate_cost, but could be more specific about what 'context' and 'price' refer to.

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 guidance on when to use or not use this tool versus siblings like compare_models or find_best_value. No prerequisites or exclusions are mentioned.

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, so the description carries a lower burden. It adds no behavioral details such as pagination behavior, sorting, or error conditions. The description's only behavioral clue is the cursor parameter, but it is not explained.

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 of approximately 10 words. Every word serves a purpose—no redundancy or 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 an output schema existing, the description is insufficient for a tool with 7 parameters and pagination. It does not explain cursor usage, how query interacts with other filters, or what the output represents beyond the schema. Sibling tools like 'search_news' indicate a need for clearer scope differentiation.

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% (all 7 parameters described in schema), baseline is 3. The tool description adds meaning for four parameters (provider, workload, context, price) beyond the schema's minimal descriptions, but does not cover limit, query, or cursor. The addition is marginal, maintaining the 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 states 'Search the pricing catalog with provider, workload, context, and price filters,' specifying the verb 'search' and the resource 'pricing catalog' with explicit filters. It distinguishes from siblings like 'benchmark_lookup' and 'calculate_cost' by focusing on searching and filtering, but does not detail the output.

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 guidance on when to use this tool versus alternatives like 'compare_models' or 'find_best_value'. The description only states what it does, lacking explicit when-to-use or when-not-to-use context.

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?

The annotation readOnlyHint=true is already present, but the description adds no additional behavioral context (e.g., pagination, sorting, rate limits, or that it returns a list of news items). The description merely restates the tool's basic function.

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 wasted words, achieving conciseness. However, it may be too terse for adequate guidance, but conciseness is still high.

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 five parameters, no required fields, and no enums, the tool is relatively simple. However, the description offers no insight into the return structure (though output schema exists), filtering behavior, or relationship to other tools, leaving gaps for an AI agent.

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 descriptions are minimal (e.g., 'query' repeated without details), and the tool description does not clarify parameter usage, such as expected formats or constraints for 'limit', 'cursor', 'source', or 'language'. Despite 100% schema coverage, the descriptions add no value.

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 the verb 'Search' and resource 'AI Pricing Hub news items', clearly indicating what the tool does. However, it could be more specific about the scope (e.g., which aspects of news), but it's sufficiently distinct from siblings like search_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?

No explicit guidance on when to use this tool versus alternatives. With siblings like search_models, the description should clarify if search_news is for textual news content versus model data, but it does not.

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