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Nanites

Nanites

by ADn-001
OverviewSchemaSourceRelated ServersDiscussionsAdmin
TypeScript
Hybrid

Get Loaded Model

get_loaded_model

Identify which models are currently loaded into GPU or CPU on the active LM Studio profile to confirm available resources before delegating tasks.

Instructions

Return the models currently loaded into GPU/CPU on the active profile's LM Studio endpoint (loaded_instances non-empty).

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": "get_loaded_model"
  }
}

Input Schema

NameRequiredDescriptionDefault
verboseNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

A3.6/5.0
Behavior3/5

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

No annotations are provided, so the description must handle safety and behavior. 'Return' implies read-only, which is helpful, but it doesn't disclose if it can fail when no models are loaded or any side effects. The parenthetical about loaded_instances non-empty hints at return condition but is minimal.

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?

One sentence, concise and front-loaded with the core purpose. No wasted words.

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

Completeness3/5

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

For a simple read-only getter with one optional parameter, the description is almost sufficient. However, the 'verbose' parameter is undocumented, and the return format is not specified (no output schema). It's adequate for a simple call but could benefit from explaining the verbose behavior.

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 single parameter 'verbose' is not described in the schema (0% coverage). The description doesn't mention it at all, so the agent has no idea what verbose does. However, having only one parameter, the description's omission is a significant gap, but the baseline for no schema coverage is 4, and the description adds no value here, so a 3 might be fair. Given it's a simple boolean, I'd give 3.

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?

The description clearly states the tool returns currently loaded models from the LM Studio endpoint, distinguishing it from list_models (likely all available) and load_model/unload_model. Specific resource and action are clear.

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?

It implies use when you need to know what's loaded, but doesn't explicitly contrast with load_model or unload_model or mention when not to use. Among siblings, list_models is a potential alternative but no differentiation is given.

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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