check_modelCheck whether an LLM model id is safe to use, deprecated, or retired, and what to migrate to. Accepts the exact string used in code (e.g. 'gpt-4o', 'claude-sonnet-4-5-20250929', 'anthropic/claude-opus-4-1'). Call this before writing or changing any hardcoded model id.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | Model id or alias exactly as it appears in code, e.g. 'gpt-4o'. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Description adds value beyond annotations (readOnlyHint, openWorldHint) by clarifying the tool returns status and migration recommendation. No contradiction. Could mention that it does not modify any state, but annotations already cover safety.
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, no fluff. First sentence clearly states purpose and outputs. Second sentence provides actionable usage instruction. 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?
Given one parameter and no output schema, the description adequately explains the tool's purpose and when to use it. It could mention the structure of the return value, but the tool is straightforward and the description suffices for an agent to invoke it 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% (single parameter with description). Description adds concrete examples ('gpt-4o', 'claude-sonnet-4-5-20250929') and reinforces that it must be the exact code string. This exceeds the schema's basic 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?
Title 'Check a model's lifecycle status' and description specify verb ('check'), resource ('model lifecycle status'), and scope ('safe, deprecated, retired, migration'). Clearly distinguishes from sibling tools like search_models or suggest_replacement by focusing on exact model IDs and lifecycle status.
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?
Explicitly states 'Call this before writing or changing any hardcoded model id.' Provides usage context (exact string from code). Does not explicitly mention when not to use, but sibling tools offer alternatives for broader searches or replacements.
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 distinct purpose: check_model validates a specific model ID, list_retiring_models shows upcoming retirements, recent_lifecycle_changes shows recent events, search_models allows filtered queries, and suggest_replacement provides alternatives. No overlap.
Most tools follow verb_noun pattern (check_model, list_retiring_models, search_models, suggest_replacement), but 'recent_lifecycle_changes' uses an adjective instead of a verb, breaking the pattern slightly.
With 5 tools, the server is well-scoped for its domain of model lifecycle management. Each tool addresses a core need without unnecessary bloat.
The tool set covers key lifecycle tasks: checking status, listing retirements, recent changes, searching, and suggesting replacements. Minor gaps exist (e.g., no tool to view all models without filtering, but search_models with no filter achieves that).