Minutes of AI

Check model ids

check_model_ids

Read-only

For each model id (as written in code): the Model Tracker row, its lifecycle status, retirement date and precision, replacement, source page and when it was last checked. Match order: exact API id, API id in another case, tracker slug, display name; match says which. Ids the tracker does not hold are returned with found: false.

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": {
      "ids": "<ids>"
    },
    "name": "check_model_ids"
  }
}

Input Schema

Name	Required	Description	Default
`ids`	Yes

Output Schema

Name	Required	Description	Default
`data`	Yes
`license`	No
`attribution`	Yes

Schema Changelog

Changes observed during successful MCP inspections.

Changed2 schema fields changedOct 9, 2026

addedOutput schema / properties / data / items / properties / datePrecision

Added value: +{
+  "description": "as in list_deadlines: exact: the stated day; earliest: the earliest possible day as the lab states it; not_before: not before this day, no retirement date is set and the model is not deprecated",
+  "enum": [
+    "exact",
+    "earliest",
+    "not_before",
+    null
+  ],
+  "type": [
+    "string",
+    "null"
+  ]
+}

changedOutput schema / properties / data / items / properties / retirementDatePrecision / description

Previous value: -"exact: the stated day; earliest: the earliest possible day as the lab states it; tentative: a not-before date, the model is not deprecated"New value: +"exact: the stated day; earliest: the earliest possible day as the lab states it; tentative: deprecated name of not_before (read datePrecision)"

AddedOct 8, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already establish this as a read-only, closed-world operation, so the bar is lower. Beyond that, the description adds real behavior: the deterministic match order (exact API id, API id in another case, tracker slug, display name) with a `match` field reporting which one hit, and the important disclosure that unknown ids are not errors but return found: false.

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?

Dense but front-loaded: the return payload comes first, then match semantics, then the not-found case. No filler sentences and nothing repeated from the schema, though the field enumeration is lengthy enough to read as a packing list.

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?

An output schema exists, so the description needn't fully specify return values, yet it helpfully names the key result fields. For a read-only, closed-world lookup the picture is nearly complete; the remaining gap is guidance on when this beats resolve_model/get_model and the batching constraints.

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 0%, so the description has to compensate and it partially does: 'as written in code' explains that the values are arbitrary id strings, and the match-order sentence explains how each string is resolved. It does not mention the schema's 50-item cap or per-id length limits, so coverage remains incomplete.

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

Purpose4/5

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

States a specific verb+resource: batch-checking each model id (as written in code) against the Model Tracker and returning row data, lifecycle status, retirement info, replacement, source and last-checked date. The purpose is unambiguous, but it does not differentiate itself from the adjacent sibling resolve_model, which sounds like it does a similar id-to-model lookup.

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

Usage Guidelines3/5

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

Usage is only implied by the phrase 'as written in code' — i.e. use it to validate ids harvested from source. No explicit when-to-use, when-not-to-use, or comparison against resolve_model / get_model, which are the most likely alternatives an agent would weigh before calling this.

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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