Trust Engine MCP

model_retirements

List upcoming AI deprecations and shutdown dates with verified source URLs across Anthropic, OpenAI, Gemini, Bedrock, and Mistral; filter by provider, model, or cutoff date.

Instructions

Use to list upcoming AI model retirements and deprecations instead of searching vendor pages: verified dates for Anthropic, OpenAI, Google Gemini API and Vertex AI, AWS Bedrock and Mistral, each with its official source URL and the date it was checked. Filters: provider (e.g. "openai", "anthropic" also finds Claude on Bedrock), model (id or name), before_date (YYYY-MM-DD), include_past, text_models_only (skip audio, image and embedding models). Free, read-only.

Tool call template

This tool declares no required parameters. Add optional parameters from the input schema as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {},
    "name": "model_retirements"
  }
}

Input Schema

Name	Required	Description	Default
`model`	No
`provider`	No
`before_date`	No
`include_past`	No
`text_models_only`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 12, 2026

TDQS

A4.5/5.0

Behavior4/5

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

With zero annotations, the description carries the load and does disclose provenance (official source URL plus date checked), scope of verification, and safety profile (“free, read-only”). It omits details like pagination, rate limits, or how often data is refreshed beyond the per-record checked date.

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

Conciseness5/5

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

Two sentences, front-loaded with purpose and the alternative it replaces, then a compact clause covering all filters. Every phrase carries information; nothing is repeated from structured fields verbatim.

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

Completeness4/5

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

For a read-only, five-parameter lookup with an output schema, the definition covers purpose, sources, record contents, and every filter, so an agent can call it correctly. Minor gap: no statement on result limits or ordering of returned retirements.

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

Parameters5/5

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

Schema description coverage is 0%, and the description documents all five parameters with real meaning: provider matching behavior (“openai”, “anthropic” also finds Claude on Bedrock), model as id or name, before_date format YYYY-MM-DD, include_past, and text_models_only excluding audio/image/embedding models. This fully compensates for the bare schema.

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 (“list upcoming AI model retirements and deprecations”) plus the exact provider coverage (Anthropic, OpenAI, Gemini/Vertex, Bedrock, Mistral). An agent can tell this apart from catalog/pricing siblings 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 Guidelines4/5

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

Explicit routing guidance: “Use to list upcoming AI model retirements and deprecations instead of searching vendor pages.” The alternative is framed as external search rather than a named sibling, and there is no explicit when-not-to-use condition relative to tools like check_my_models or pricing.

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

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