AI model deprecation calendar: announced and shutdown dates, replacement models, and how many days of migration notice each provider actually gives (median/min/max). Every entry is verified against the provider own deprecation page.
| Name | Required | Description | Default |
|---|---|---|---|
| provider | No | openai, anthropic, google, mistral, cohere, azure-openai; omit for all |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are present, so the description bears the full transparency burden. It does add a useful behavioral detail: every entry is verified against the provider's deprecation page. However, it does not disclose return shape, pagination, or any access/side-effect considerations beyond the implied read-only 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?
A single, information-dense sentence front-loads the core purpose and then lists the specific data dimensions. Every clause adds value, including the verification note at the end. No unnecessary words or repetition.
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 tool with one optional parameter and no output schema, the description adequately specifies the kind of data returned. It could be more complete by describing the output structure, but the level of detail is sufficient for an agent to invoke the tool 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?
The input schema provides 100% coverage for the sole parameter, listing valid provider values and the omission behavior. The description adds no additional meaning to the parameter beyond what the schema already states, so the baseline score of 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 clearly identifies the resource as an AI model deprecation calendar with specific data points (announced/shutdown dates, replacement models, migration notice stats). It lacks an explicit verb like 'retrieve' or 'list,' and does not explicitly differentiate from the sibling tool, but the topic is distinct enough.
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 guidance is provided about when to use this tool versus get_ai_api_latency or any other alternative. The description implies a use case for deprecation information but does not state conditions, exclusions, or when a sibling would be more appropriate.
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.
The two tools target entirely distinct concerns: one measures API latency/uptime and the other tracks model deprecations. There is no overlap or plausible selection confusion between them.
Both tools follow the same get_ + descriptive noun phrase pattern. The naming style is uniform, readable, and predictable.
Two tools feels thin for a server positioned as a tracker, but each tool covers a meaningful, non-overlapping data domain. It is at the borderline rather than clearly over-scoped or under-scoped.
The server covers the two main query needs implied by its name: latency/uptime data and deprecation timelines. Minor gaps remain, such as per-model latency detail or provider-specific filtering, but agents can likely work around them.
