Skip to main content
Glama

AI model deprecation and shutdown calendar

get_model_deprecations
Read-onlyIdempotent

Check announced AI model shutdowns and replacements before pinning a version, planning migrations, or verifying if a model ID still works. Filter by provider; optionally include retired models.

Instructions

List announced shutdowns of AI models (OpenAI, Anthropic, Mistral, Azure OpenAI and others) with announcement date, shutdown date, recommended replacement and the provider page each entry was verified against, plus how many days of notice each provider typically gives (median/min/max). Use it before pinning a model version, to plan migrations, or to check whether a model ID still works. Returns upcoming shutdowns by default; set include_past to also get already-retired models. This is calendar data, not latency; for speed use get_ai_api_latency.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
providerNoFilter to one provider, case-insensitive: openai, anthropic, mistral, azure-openai, cohere. Omit for all providers.
include_pastNoAlso return models whose shutdown date has passed (default false).

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
pastNoRetired models (only with include_past)
sourceNoHuman-readable calendar page
upcomingYesModels not yet shut down, soonest first
generatedNoUTC timestamp when the calendar was built
notice_period_daysNoPer provider: models counted and median/min/max days between announcement and shutdown

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld), so the bar is lower. The description adds genuinely useful behavior beyond them: default scope is upcoming shutdowns only, include_past widens it, and entries carry a provider page they were verified against. It stops short of describing caching, freshness, or rate limits, so not a full 5.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Three sentences, front-loaded with what the tool returns, then usage, then the sibling disambiguation. Every clause carries information, though the long returned-field enumeration is dense enough that it approaches a data dump.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a two-parameter read-only lookup with a full output schema and complete annotation coverage, nothing an agent needs is missing: scope defaults, filter behavior, replacement guidance, and sibling routing are all present.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so both parameters are already documented with valid provider values and the include_past default. The description restates the include_past effect ('also get already-retired models') and omits provider filter syntax, adding little beyond the schema. Baseline 3 applies.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb (list) and resource (announced AI model shutdowns), enumerates the fields returned (announcement date, shutdown date, replacement, verification page, notice statistics), and explicitly distinguishes itself from the sibling latency tools. An agent can identify this tool without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines5/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives explicit when-to-use triggers ('before pinning a model version, to plan migrations, or to check whether a model ID still works') and names the alternative for a different need ('for speed use get_ai_api_latency'). Both the selection condition and the exclusion are stated.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.