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Glama

Is this model deprecated, and what replaces it?

check_model_deprecation
Read-onlyIdempotent

Verify if an LLM model ID is active, deprecated, or retired. Get its shutdown date and recommended replacement before coding or when a model call fails.

Instructions

Check if an LLM model id is active, deprecated or retired, its shutdown date, and the recommended replacement, from the provider's own deprecation page (OpenAI, Anthropic, Google, etc.). Use before hard-coding or calling a model, or when a model call fails with "model not found / deprecated". Returns summary first, then verdict, recommended_action (keep_using | plan_migration | switch_now | verify_with_provider), replacement, shutdown_date, evidence rows and freshness. Example: {"model":"gpt-4-0613"}. Free within the daily quota, then $0.003 via x402. Alias: get_model (param "id").

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
idNoDeprecated alias of model.
modelNoModel id as used in the API call. Examples: gpt-4o, claude-3-opus-20240229, gemini-1.5-pro.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.4/5.0
Behavior5/5

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

Adds substantial context beyond the readOnly/idempotent/openWorld annotations: cost behavior ('Free within the daily quota, then $0.003 via x402'), the provenance of the data (provider's own deprecation page), and the response ordering with freshness. For a read-only lookup with no output schema, this is rich behavioral disclosure.

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?

Front-loaded with purpose, then usage triggers, then return fields and pricing — a logical order with little waste. The return-field enumeration and alias/cost sentences make it dense but each carries information the agent needs.

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?

With no output schema, the description compensates by enumerating the return fields (summary, verdict, recommended_action enum values, replacement, shutdown_date, evidence, freshness). Combined with annotations, triggers, cost and alias coverage, an agent has everything needed to call it correctly.

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 coverage is 100%; the schema already documents both 'model' and its deprecated alias 'id' with examples. The description reinforces this with an inline example and the alias note, but adds little that the schema does not already say, so 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 and resource — check an LLM model id's lifecycle status — plus the concrete outputs (shutdown date, replacement) and the authority it reads from (the provider's own deprecation page). An agent can immediately tell this apart from siblings like check_api_status or list_recent_api_changes.

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

Usage Guidelines4/5

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

Gives explicit trigger conditions: 'Use before hard-coding or calling a model, or when a model call fails with "model not found / deprecated".' That is clear context for invocation. It stops short of naming when not to use it or which sibling to prefer in adjacent cases, so it is not a full 5.

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