isitdeprecated

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

With no annotations, the description carries the burden. It discloses what the tool checks (deprecation, retirement, silent changes) and that it accepts any spelling. It does not mention return format or error behavior, but for a check tool this is adequate.

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 concise sentences: what it does, examples, and when to use. No wasted words, every sentence contributes.

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 tool with no output schema, the description provides enough context for an agent to select and invoke it. It could explain return values but that is not required given the clarity of 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 coverage is 100%, so baseline is 3. The description adds value by explaining the parameter accepts any spelling and provides concrete examples, going beyond the schema's simple 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 checks whether an AI model identifier is deprecated, retiring, or silently changed. It explicitly distinguishes from siblings by focusing on a single identifier and mentions the specific resource (model identifier).

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

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

The description gives explicit guidance to call this tool before writing a model identifier into code. It does not mention when not to use it or explicitly name alternatives, 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?

With no annotations, the description carries the transparency burden. It discloses the key behavior (reporting retired or scheduled status) but does not mention whether the operation is read-only, error handling for invalid IDs, or output format. It adds some context beyond the name but lacks edge-case disclosures.

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, no filler. The first sentence combines action and result; the second gives usage guidance. Every word 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?

For a simple batch-check tool with one parameter and no output schema, the description is adequately complete. It states the purpose, scope, and typical use case. It lacks details on return format, but that may be clear from the tool's nature.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

The schema already documents the single parameter with 100% coverage (up to 100 identifiers). The description merely restates 'many model identifiers,' which adds no new 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 clearly states the tool's function: checking many model identifiers at once and reporting which are retired or scheduled for retirement. The phrase 'many model identifiers' distinguishes it from the sibling check_model, and the specific outcome (retired or scheduled) clarifies its purpose.

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

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

The description provides explicit usage context: 'Use when auditing a codebase or config.' While it doesn't name alternative tools, the context implies a batch check scenario versus a single check_model, offering clear when-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 provided, the description carries the behavioral disclosure burden. It adds useful context by specifying the kind of changes included and the 'one entry per real upstream change' deduplication behavior, but it does not mention whether the operation is read-only, authentication requirements, or potential rate limits.

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 that conveys the essential scope quickly. There is slight redundancy ('removals' and 'removed capabilities' overlap), but overall the structure is concise and no words are 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?

The tool has no output schema and no annotations, but it is a simple list operation with two optional parameters. The description explains the content and deduplication behavior, but it lacks details about the time window covered, return format, and pagination/limit behavior, making it adequate rather than fully 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 description coverage is 0%, so the description must compensate by explaining parameters, but it does not mention limit or min_severity. The parameter names and the enum for min_severity are partially self-explanatory, but no additional meaning is added 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 returns recent breaking changes across AI model APIs and enumerates specific categories (price moves, context-window cuts, removed capabilities). This is a specific verb+resource combination that distinguishes it from siblings like check_model and upcoming_retirements, which concern current state and future changes respectively.

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 its alternatives. The sibling tool names imply complementary use cases, but the description does not state when to prefer recent_changes over check_model or upcoming_retirements, nor any exclusions.

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

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It does convey that the tool filters by announced retirement date and sorts results, but it does not explain how the 'horizon' is determined or whether the output includes any additional fields or pagination. For a read-only listing tool, this is moderately transparent but not rich.

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 that conveys the core action, subject, and sorting order without wasted words. It is appropriately sized for the tool's simplicity.

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's simplicity (one optional parameter, no output schema), the description is mostly complete but has a key gap: it fails to explain that the horizon is the within_days parameter. Without this connection, an agent might not know how to control the horizon. The description also assumes familiarity with 'models' in the domain 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?

The schema has one parameter, within_days, with 0% description coverage. The description mentions 'inside a horizon' but never explicitly connects this to the within_days parameter, leaving the agent to infer that the horizon is the parameter's value. It does not specify that within_days is measured in days or that it defaults to 90, so the parameter's meaning is under-explained.

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 function: listing models with an announced retirement date within a specified horizon, sorted by soonest date first. The verb 'List' identifies it as a read-only retrieval operation, and the resource ('models with an announced retirement date') and ordering ('soonest first') distinguish it from siblings like 'recent_changes'.

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

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

The description implies the context of use: to find models retiring within a time horizon. It does not explicitly mention alternatives or exclusions, but the 'soonest first' ordering and 'inside a horizon' phrasing suggest this is for planning ahead. The sibling names (check_model, check_models, recent_changes) imply different purposes, so the description gives enough context without explicitly naming alternatives.

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