ai-model-lifecycle-index

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

No annotations are provided, so the description carries the full transparency burden. It adds useful non-schema context about availability and cost ('Free during the public preview; metered via x402 when live'), but it does not disclose output format, pagination, or what 'lifecycle export' includes.

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 one tightly front-loaded sentence with no redundant filler. The scope statement comes first, and the pricing/availability note is compact and relevant.

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 one-optional-parameter tool with no output schema, the description plus schema is mostly sufficient for an agent to invoke it correctly. The main gap is the lack of clarity on return shape and how the filter narrows the export.

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 provider parameter is already described as an 'optional filter.' The description adds cross-vendor scope but does not clarify how the optional provider filter interacts with the 'all tracked models' claim.

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 clear action ('full cross-vendor lifecycle export') and explicitly scopes to 'all tracked models.' It distinguishes itself from vendor-specific siblings by emphasizing cross-vendor coverage, though it does not name alternatives.

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 phrase 'all tracked models' and 'cross-vendor' imply this is the comprehensive export, which gives some context versus siblings like model_lifecycle. However, it does not explicitly say when to use this tool over alternatives or when to avoid 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?

There are no annotations, so the description carries the behavioral burden. It usefully adds the 'FREE' and 'fast' traits and specifies the return content (callability, sunset date, days remaining). However, it does not disclose behavior for invalid/expired model IDs, response shape, or potential error conditions, which leaves some transparency gaps.

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 three short sentences with no filler. It front-loads the most decision-relevant facts: cost, purpose, and when to use it.

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 one-parameter tool, the description gives enough context to recognize when to use it and what it returns. However, with no output schema and no annotations, it does not explain edge cases like unknown model IDs, and the exact response format is left unspecified.

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% and the description never explicitly references model_id or explains how to provide it. The single parameter name is self-explanatory, which prevents a score of 1, but the description does not add meaningful semantic 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 operation: checking whether an AI model is still callable, and it adds the deliverable (sunset date and days remaining). The verb 'check' plus the resource 'model' makes the purpose clear. It does not explicitly differentiate from siblings such as model_lifecycle or get_model_lifecycle_all, but the tool's niche is evident from the 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?

'Use before depending on a model at runtime' gives a concrete, decision-oriented usage context, so an agent knows when this check is appropriate. It does not name alternatives or explicitly spell out when not to use it, but the provided context is clear and actionable.

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 behavioral burden. It does disclose the returned content (statuses, dates, migration target, URL), which is useful. However, it does not disclose error behavior for unknown model_ids, what 'days until shutdown' returns for active/preview models (null vs zero), date formatting, caching, or any rate limits. For a read-only query tool this is a moderate gap, not a critical one, hence a 3.

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 dense sentence that front-loads the core purpose and then lists the deliverable fields compactly. The leading 'FREE.' is a minor token that adds no clear value and slightly muddies structure, but the rest is efficient with no redundancy. It earns a 4 rather than 5 due to that stray prefix.

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, no-output-schema tool with no annotations, the description does a solid job enumerating what the caller receives, which is the primary thing an agent needs. The gaps are error handling for invalid model_id and edge-case semantics of 'days until shutdown' for non-deprecated models. These are worth noting but not severe enough to drop below a 4 given the tool's low complexity.

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 model_id well documented via canonical examples in the schema itself. The description adds only that the tool covers a single model's lifecycle, lightly reinforcing that model_id selects which model. Since the schema already carries the parameter detail, the description adds no meaningful syntax or format information beyond it, so the 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 specifies the resource ('one AI model') and enumerates the returned fields (status, dates, days until shutdown, migration target, source URL), which makes the purpose clear. The singular framing implicitly distinguishes it from siblings like get_model_lifecycle_all (all models) and upcoming_deprecations (future events), though it never names them explicitly. It lacks a strong verb, using a noun phrase ('Lifecycle of...') instead, which costs it 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?

Usage context is implied but not stated: the singular 'one AI model' suggests this tool is for querying a specific model's full lifecycle, while siblings get_model_lifecycle_all, is_model_live, and upcoming_deprecations cover other cases. No alternative tools are named and no when-to-use/when-not-to-use guidance or exclusions are given. The 'FREE' prefix hints at cost behavior but is not explained. An agent must infer the boundary against siblings on its own.

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 is the sole source of behavioral info. It states that results are limited to upcoming shutdowns within N days, that migration targets are returned, and that the service is free. It does not mention data source, refresh cadence, rate limits, or output shape, but these are less critical for a simple no-side-effect lookup.

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 short and readable, front-loading the core action and scope. The leading 'FREE.' is arguably noise for an invocation decision, but not misleading; 'Default 90 days' repeats the schema.

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-parameter tool, the description explains what is returned, which providers are covered, and the default window. Because there is no output schema, a description of the exact response format would be useful if this were more complex, but the current coverage is adequate.

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 description need not expand parameters much. It reinforces that N is a day window and the default is 90, but adds no semantics beyond the schema's own text.

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's function: listing AI models scheduled for shutdown within a configurable window across four providers, with migration targets included. This distinguishes it from siblings like is_model_live (current availability) and get_model_lifecycle_all (broader lifecycle 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 use case is implied by the wording: call when you need upcoming deprecations and migration targets. However, it never explicitly says when to prefer this over model_lifecycle/get_model_lifecycle_all, and offers 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.