Modelmeter

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

With no annotations, the description takes full burden. It discloses tolerance for ID variations (dots vs dashes, suffixes, prefixes) and what it checks (scheduled retirements, past retirements, breaking changes). However, it does not explicitly state it is read-only or describe failure modes.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with purpose, no filler. Every sentence adds value: purpose, behavior, usage. Excellent conciseness.

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 purpose, matching logic, and usage. Lacks explicit output description (no output schema), but for a check tool with one array parameter, it is adequately complete for an agent to understand and invoke.

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% with an example. The description adds significant semantic meaning by explaining the purpose of the parameter and the matching logic, going beyond the schema's basic type and example.

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 checks model IDs against retirement records, with a friendly opener 'Is my stack okay?' The verb 'Check' and resource are explicit, and it distinguishes from siblings like list_deprecations by focusing on specific models.

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

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

The description advises calling 'at startup or in CI with the models you depend on,' providing clear usage context. It does not explicitly exclude alternatives or contrast with siblings, 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?

With no annotations, the description carries the full burden. It discloses that it returns a cost breakdown and markup info. However, it does not explicitly state that the operation is read-only or safe, but the name 'estimate' inherently suggests non-destructiveness.

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 concise sentences with no filler. The description is front-loaded, starting with the main action and result.

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 absence of an output schema, the description adequately explains the return format. All necessary aspects (purpose, parameters, output) are covered. Sibling tools do not overlap.

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 schema already describes all parameters. The description adds value by explaining the output (cost breakdown) and guiding the user to use list_models for model IDs. This exceeds the baseline of 3.

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 USD cost for an LLM API call for a given model and token counts, and explicitly lists the return values (input/output/total cost, plus reseller markup). It is distinct from sibling tools like list_models or get_price_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?

The description implies usage for cost estimation but does not explicitly state when to use this tool versus alternatives, nor does it provide any exclusions or prerequisites. The siblings are different functions, but guidance on when not to use is missing.

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 provided, so description carries full burden. Clearly lists returned data: normalized pricing, capabilities, availability, reseller markup, price-history summary. Does not mention destructive behavior or auth, but implies read-only operation.

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 packs detailed information efficiently. Slightly long but no wasted words. Could be split for readability, but current structure is acceptable.

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?

No output schema, so description must explain return values. It comprehensively lists components: pricing, capabilities, availability, markup, price history (including launch vs current, percent change, dates, all-time high/low). Covers complexity well.

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% with one parameter 'id' described as 'Model id in provider/model form'. Description adds no additional semantics beyond schema, so baseline 3.

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 specifies 'Unified card for one model in a single call' with clear verb and resource, and distinguishes from sibling tools like list_models (multiple models) and get_price_history (history only).

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?

States when to use (getting comprehensive single-model data) but does not explicitly exclude cases or mention alternatives beyond implied context. Lacks explicit 'when not to use' guidance.

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 provided, so description carries full burden. It mentions data is a time-series with dated snapshots per-Mtok rates but does not disclose caching, data freshness, error handling, or behavior when no data matches. Minimal 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?

Two sentences with no unnecessary words. Front-loaded with purpose, then explains data point meaning. Efficient and clear.

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?

Lacks output schema and annotations. Does not explain time granularity, page size, or how provider and model interact (mutually exclusive? required?). Agents need more detail for correct usage.

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% with clear descriptions for each parameter. Tool description adds context about return format (dated snapshot) but does not enhance parameter understanding beyond schema. 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 clearly states it provides historical pricing time-series for a model or provider, using specific verb-resource combination. It distinguishes from siblings like estimate_cost or list_models, which cover different functionalities.

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 estimate_cost for current pricing or list_events for deprecation events. The description lacks context on prerequisites or exclusive use of parameters.

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 provides basic behavior - read operation returning sorted retirement rows - but does not disclose rate limits, whether it's read-only, or pagination details. The description is adequate but lacks enrichment beyond the obvious.

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, clear and front-loaded with key information. The first sentence is slightly jargon-heavy but still effective. No wasted words.

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 no output schema, the description gives a good overview of what rows contain (model, date, runway, migration target). It could list exact fields or mention pagination, but it's sufficient for an AI agent to understand the tool's purpose.

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 cover all parameters. The description adds no extra semantic value for parameters beyond noting the default scheduled status, which is already in 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 per-model retirement rows with shutdown date, runway, and migration target. It distinguishes from siblings like list_models (general model listing) and get_model (single model detail) by focusing on deprecation information.

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 for viewing retirement schedules (default scheduled) and filtering by status or provider, but does not explicitly contrast with sibling tools or provide when-not-to-use guidance.

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 burden. It explains what events are covered and severity filtering, but lacks details on pagination, ordering, or response structure. The behavior is adequately described for a list tool but not comprehensive.

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, front-loaded with the core purpose, and each sentence adds essential information. No extraneous words; the severity explanation is concise yet clear.

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?

While the description covers the main purpose and key filters, it lacks details on return format, pagination behavior, or default ordering. Given no output schema and 7 parameters, additional context about result structure would improve completeness.

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 baseline is 3. The description adds value by explaining severity levels and giving examples of event types, which goes beyond the schema. It provides context that helps select appropriate filters.

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 a changelog of events (deprecations, price changes, launches) and distinguishes it from sibling tools like list_deprecations by covering broader event types. The verb 'list' and resource 'events' are explicit.

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 hints at when to use it ('filter by severity to find what demands action') but does not explicitly state when not to use it or compare with alternatives. More direct guidance on selecting this tool over siblings like get_price_history would improve the score.

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

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

No annotations are provided, so the description carries full burden. It does not disclose that this is a read-only operation, auth requirements, rate limits, or pagination behavior. Only the limit parameter hints at pagination, but the description is silent.

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, focused sentence with no wasted words. It is front-loaded with the core action.

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 3 parameters and no output schema, the description is only one sentence. It does not explain return format, pagination details, or what 'available' means. More context 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 coverage is 100%, so baseline is 3. The description adds value by noting optional filtering by provider, but does not expand on limit or include_unavailable beyond 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 specific verb+resource: 'List available models with their current pricing.' This distinguishes from sibling tools like get_model (single model) or estimate_cost (cost estimation).

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 mentions optional filtering by provider, giving clear context. However, it does not explicitly state when not to use or point to alternatives like get_model for single model queries.

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