Check whether an AI model identifier is deprecated, retiring, or has silently changed (price, context window, capabilities). Accepts any spelling a provider or SDK uses, e.g. 'gpt-4-turbo', 'azure/gpt-4.1-mini', 'claude-opus-4-1-20250805'. Call this before writing a model identifier into code.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model identifier to check |
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.
Add one secure layer between your agents and this server.
Each tool has a clear, distinct purpose: check_model for a single identifier, check_models for batch auditing, recent_changes for historical breaking changes, and upcoming_retirements for future scheduled retirements. The descriptions reinforce the boundary between singular/plural and past/future, leaving no ambiguity.
Tool names are all snake_case and readable, but the pattern is mixed: 'check_model' and 'check_models' follow a verb_noun imperative style, while 'recent_changes' and 'upcoming_retirements' are descriptive noun phrases with no verb. The inconsistency in grammatical form prevents a higher score.
Four tools is a well-scoped size for a deprecation-checking server. Each tool covers a distinct need (single lookup, batch lookup, recent activity, future events) without unnecessary redundancy or missing core functionality.
The tool surface covers the full lifecycle of deprecation awareness: checking individual models, auditing multiple at once, seeing what has already changed, and planning for upcoming retirements. There are no obvious dead ends or missing operations for the stated purpose.