Modelmeter

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

No annotations are provided, so the description must carry the full burden. It discloses ID matching tolerance and that it checks retirements and breaking changes, but lacks details on response format, error behavior, or whether it is read-only.

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 succinct, front-loaded with purpose, and every sentence adds value. 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 and one parameter, the description covers purpose, usage timing, and input flexibility. It could be improved by mentioning the response structure or any limits, but overall it is quite complete.

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 a good example. The description adds extra meaning by explaining ID matching flexibility, which helps agents understand acceptable input formats.

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 a list of model IDs against scheduled retirements and breaking changes, with specific details on ID matching tolerances. It distinguishes itself from siblings like list_deprecations by focusing on specific user-provided 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 explicitly recommends calling it at startup or in CI with dependent models. It does not explicitly mention when not to use it or contrast with sibling tools, but the context is clear.

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 the full burden. It mentions return values (input/output/total cost, reseller markup) but does not disclose whether the tool is read-only, authentication requirements, or any potential rate limits. The behavior described is adequate but not thorough.

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 wasted words. It is front-loaded with the primary purpose and provides essential details efficiently.

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 lacking an output schema, the description clearly states what the tool returns (input/output/total cost, markup). For a simple estimator with three parameters, this is largely complete. Minor gap: no mention of whether the estimate is real-time or cached.

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 parameters are already well-documented. The description reinforces the currency (USD) and references list_models for model IDs, adding marginal value. However, it does not explain 'reseller markup' in detail.

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 estimates the USD cost of an LLM API call using a given model and token counts. It also distinguishes itself from siblings by focusing on cost estimation rather than listing models or checking dependencies.

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 implicitly indicates when to use the tool (when needing cost estimates), but it does not provide explicit guidance on when not to use it or mention alternatives like get_price_history for historical data.

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 must disclose behaviors fully. It lists the returned data (pricing, capabilities, etc.) and indicates a read operation. However, it does not mention error handling (e.g., if model ID is invalid), rate limits, or other 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?

The description is a single sentence that packs many details without being overly verbose. It efficiently conveys the tool's output, though it could be slightly more structured (e.g., bullet points).

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 main output aspects but omits error cases, pagination (not applicable here), or any behavioral constraints. Given the single parameter and lack of output schema, additional context on error responses or availability 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?

The only parameter 'id' has 100% schema coverage. The description adds value by providing an example and specifying the required format 'provider/model form', clarifying beyond the schema's generic 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 clearly states the tool returns a 'unified card for one model' with specific details like pricing, capabilities, availability, reseller markup, and price history. It distinguishes itself from sibling tools like list_models (which lists many models) and get_price_history (possibly price-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?

The description implies this tool is for obtaining a comprehensive overview of a single model, but it does not explicitly state when to use it versus alternatives (e.g., list_models for browsing, check_model_dependencies for dependency checks). No exclusions or prerequisites 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?

No annotations are provided, so the description must carry the full burden. It describes the output as 'time-series' with 'dated snapshots', implying a read-only query. However, it does not disclose any rate limits, authorization requirements, or potential side effects. The behavior is adequately described but lacks depth.

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 extremely concise: two sentences, 20 words. Every word adds value, instantly conveying the tool's purpose and output format. No fluff or repetition.

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 and 4 parameters (all optional), the description is adequate but incomplete. It mentions 'dated snapshot' but does not specify the exact return structure (e.g., array of objects with date and price fields). For an agent to use the output correctly, more detail on the response format would be helpful.

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 each parameter having a clear description. The tool description adds minimal extra meaning (e.g., 'per-Mtok rates', 'dated snapshot'). Since the schema already handles parameter semantics well, 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 clearly states it provides historical pricing time-series for a model or provider, with specific mention of per-Mtok rates. This distinguishes it from sibling tools like estimate_cost (which estimates current cost) and list_models (which lists models). The verb 'get' is implicit in the tool name and description.

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 does not provide any guidance on when to use this tool versus alternatives. No context on prerequisites, when not to use, or typical use cases. The agent receives no help in deciding between get_price_history and siblings like estimate_cost or list_events.

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 default status (scheduled), sorting by shutdown date, and mentions key output fields. However, it does not specify destructive potential, authentication needs, or detailed output structure. The absence of an output schema amplifies the need for more 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 sentence that efficiently conveys the core functionality, defaults, and sorting. It is front-loaded with the main verb and resource. While compact, it could benefit from a bit more structure to separate the default behavior from the general 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?

Given 3 parameters and no output schema, the description fairly completely explains what the tool returns (model, date, runway, migration target) and its defaults. The absence of an output schema is a gap, but the description compensates by naming the key fields. It is adequate for an agent to use 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% with descriptions for all three parameters. The description adds value by explaining the default status (scheduled) and sorting behavior, which are not in the schema. This goes beyond the baseline of 3 for high schema coverage.

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 returns per-model retirement rows with shutdown date, runway, and migration target. It distinguishes itself from siblings by focusing on deprecations, a topic not covered by other tools like check_model_dependencies or list_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 when-to-use or when-not-to-use guidance is provided. The default behavior and sorting are mentioned, but no alternatives or exclusions are discussed. The agent can infer usage from the purpose, but explicit guidelines are 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?

With no annotations, the description effectively communicates the tool's behavior as a changelog with filtering capabilities. It details severity categories but could mention aspects like pagination or data freshness.

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 concise yet informative, front-loading the purpose and then explaining the key filtering dimension (severity). Every sentence is necessary 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?

Given no output schema, the description gives a good sense of what the tool returns (events with dates, model changes) and how to filter. It is adequate for an agent to decide usage, though more detail on return 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?

Although schema coverage is 100%, the description adds value by explaining the severity parameter's semantics in detail and how it connects to consumer actions. It also clarifies the overall event categories, enhancing understanding 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 identifies the tool as 'the changelog of record for the model layer' covering deprecations, price changes, launches, and market events. It distinguishes itself from sibling tools like list_deprecations by being broader, and explains filtering by severity to pinpoint actionable items.

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 strong guidance on when to use the tool and how to filter by severity to find actions. It explains each severity level but does not explicitly compare with siblings or mention when not to use 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?

No annotations are present, so the description must fully disclose behavioral traits. It does not mention pagination, rate limits, authentication requirements, or default behavior (e.g., returns all providers if no filter is given). The phrase 'current pricing' is inherent to the tool's purpose and adds little transparency.

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 concise with a single sentence that front-loads the core functionality. However, it could be slightly more structured to include defaults or behavior, but it remains succinct without 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 and no annotations, the description should provide more context about return format, pagination, and defaults for parameters like limit and include_unavailable. It only covers the basic listing action and optional provider filtering, leaving 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 coverage is 100%, so baseline is 3. The description adds no extra meaning beyond the schema; it only restates the filter capability. It does not explain relationships between parameters or provide usage patterns.

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 verb 'List', the resource 'available models', and includes the specific attribute 'current pricing'. It also mentions optional filtering by provider, which distinguishes it from sibling tools like get_model (singular) or estimate_cost (cost calculation).

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. For instance, it does not mention that for cost estimation one should use estimate_cost, or that for detailed model info one should use get_model. The agent is left to infer context from sibling names.

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