Nanites

List Models

list_models

Lists models available on the active LM Studio endpoint. Returns compact details by default; add verbose for complete model objects.

Instructions

List the models known to the active profile's LM Studio endpoint. Returns a trimmed shape by default; pass verbose for the full model objects.

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": "list_models"
  }
}

Input Schema

Name	Required	Description	Default
`verbose`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv1.0.0Sep 27, 2026

TDQS

A4.2/5.0

Behavior4/5

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

With no annotations present, the description carries the full behavioral disclosure burden. It discloses the key output behavior: 'Returns a trimmed shape by default; pass verbose for the full model objects.' It does not state side-effect absence or error behavior, but for a read-only list operation the default/verbose disclosure covers the main decision an agent faces.

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 with zero filler, the verb+resource front-loaded in the first sentence. The second sentence earns its place by explaining the default behavior and the parameter's effect.

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 tool with one optional boolean, no output schema, and no annotations, the description adequately covers scope and return-shape behavior. Minor omissions such as what the trimmed shape contains and behavior when the endpoint is unreachable are low-stakes for a simple list operation.

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?

Schema description coverage is 0%, so the description must compensate, and it does: 'pass verbose for the full model objects' plus the default trimmed shape gives the boolean real meaning beyond the bare schema type. It stops short of specifying which fields appear in each shape, but the semantic distinction is clear.

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 the models known to the active profile's LM Studio endpoint.' The scope phrase distinguishes it from sibling tools like nanites_listProviderModels and get_loaded_model, which concern provider-discovered or currently loaded models.

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?

The description implies a usage context by scoping the result set to the active profile's LM Studio endpoint, which separates it from provider-discovery tools. However, it never names alternatives or states when not to use this tool, so an agent must infer routing from the sibling list rather than receiving explicit guidance.

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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