Trust Engine MCP

check_my_models

Look up retirement dates, days left, vendor replacements, and official sources for AI models before choosing or deploying them.

Instructions

Use when an agent or its owner asks "will the models we use be retired?" or before choosing a model. Pass every model id or name the agent uses (e.g. ["claude-sonnet-4-5", "gpt-4o", "gemini-2.5-flash", "anthropic.claude-3-haiku-20240307-v1:0"]). For each: retirement date per platform, days left, the vendor's replacement, the official source URL, and what to do before the date. To find the models in a codebase first, use scan_for_models. Free, read-only.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "models": "<models>"
    },
    "name": "check_my_models"
  }
}

Input Schema

Name	Required	Description	Default
`models`	Yes

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.1.0Nov 12, 2026

TDQS

A4.7/5.0

Behavior4/5

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

With no annotations, the description carries the safety profile itself by declaring 'Free, read-only', which is the key fact an agent needs before calling. It also previews the per-model response contents (retirement date, days left, replacement, source URL, remediation), though it says nothing about behavior for unknown/unmatched model ids or any 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.

Conciseness4/5

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

Front-loaded with the triggering question and alternatives, then the parameter guidance. Slightly dense because the response-field list duplicates what the output schema already declares, but no sentence is filler.

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 one-parameter read tool with an output schema, the description covers trigger, alternatives, accepted input formats, and the read-only/free guarantee; the enumeration of return fields is a bonus rather than a necessity. Nothing an agent needs to invoke it correctly is missing.

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 coverage is 0% (a bare array of strings), so the description must do the work, and it does: it says to pass every model id or name the agent uses and gives concrete multi-vendor examples ('claude-sonnet-4-5', 'gpt-4o', 'gemini-2.5-flash', 'anthropic.claude-3-haiku-20240307-v1:0'), conveying accepted identifier formats that the schema alone does not.

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 ... models' for retirement) and immediately anchors it with the exact question it answers: 'will the models we use be retired?'. It is clearly separable from siblings such as scan_for_models (discovery) and model_retirements (listed but not described here).

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?

Names two explicit triggers (agent/owner asks about retirement, or before choosing a model) and explicitly routes to the alternative: 'To find the models in a codebase first, use scan_for_models.' That is a when-to-use plus a named alternative with the condition that selects it.

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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