TRAECNclaw MCP

List TraeCN Models

traecn_list_models

Read-onlyIdempotent

Lists available TraeCN UI models and the current selection, and reports when Auto hides manual models so you can choose the right model before selecting it.

Instructions

List the model names currently offered in the TraeCN UI and the current selection. If Auto hides manual models, report manualModelsHidden without changing modes. Call before select_model whenever the model choice matters.

Input Schema

Name	Required	Description	Default
No arguments

Output Schema

Name	Required	Description
`code`	No	Stable machine-readable result or error code.
`error`	No	Error identifier or diagnostic when the operation failed.
`models`	No
`status`	No	Stable operation or task status.
`taskId`	No	Stable gateway task identifier.
`changed`	No	Whether visible TraeCN state changed.
`current`	No	Currently selected model name.
`message`	No	Human-readable outcome summary.
`success`	No	Whether the operation succeeded.
`retryable`	No	Whether retrying the same call can succeed later.
`nextAction`	No	Suggested next step after a failure.
`conversationId`	No	Stable TraeCN conversation identifier.
`manualModelsHidden`	No	True when Auto mode hides manual models from the native picker.

Schema Changelog

Changes observed during successful MCP inspections.

Changed1 schema field changedv0.7.1-glama.1Nov 7, 2026

addedOutput schema / properties / manualModelsHidden

Added value: +{
+  "description": "True when Auto mode hides manual models from the native picker.",
+  "type": "boolean"
+}

First observedAug 24, 2026

TDQS

A4.6/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, idempotentHint and destructiveHint=false, so the safety profile is covered. The description still adds non-obvious behavior: that Auto can hide manual models and that this tool reports manualModelsHidden without mutating mode state. It does not say how the hidden case should be handled beyond reporting, which keeps it from a 5.

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?

Three short sentences, each doing distinct work: what is returned, the Auto-mode edge case, and the call-ordering rule. The most decision-relevant information (what you get) is front-loaded and nothing is padded.

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?

With an output schema present, return-value details need not be spelled out, and the zero-parameter schema plus rich annotations mean little else is required. The only small gap is that it does not say whether the returned model names are the exact identifiers select_model expects.

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

Parameters4/5

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

The tool takes no parameters, so there is nothing for the description to disambiguate; the baseline for a zero-parameter tool applies. No meaning is added or lost.

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?

The description names a specific verb (list) and a specific resource (model names offered in the TraeCN UI) plus an extra output (the current selection). It is clearly distinguished from the sibling select_model, which performs a mutation rather than a read.

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?

It states an explicit ordering rule: call this before select_model whenever model choice matters, naming the alternative tool and the condition that triggers it. The Auto-mode caveat also tells the agent to read the reported flag rather than trying to change modes, which is a concrete when-not behavior.

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