Recent breaking changes across AI model APIs: price moves, context-window cuts, removed capabilities, removals. One entry per real upstream change.
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | ||
| min_severity | No | medium |
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.
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.