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Glama

models

List LLMs offered by llama-server and show each one's loaded or unloaded status; in router mode, the first request naming an unloaded LLM waits while it loads.

Instructions

List the models llama-server offers, with their status.

    A single-model llama-server always reports `loaded`. In router mode a model is `unloaded` until
    a request names it, and the first such request waits while it loads.
    

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A3.7/5.0
Behavior4/5

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

With no annotations provided, the description carries the full behavioral burden and does disclose meaningful traits: single-model servers always report loaded, while router-mode models are unloaded until a request names them and the first such request waits for loading. It does not mention read-only safety or idempotency, but the listing nature is implicit.

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?

The description is two tightly scoped parts: a front-loaded purpose sentence followed by essential status semantics. Every sentence earns its place with no redundancy.

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?

Given the simple listing purpose, an empty input schema, and an existing output schema, the description covers what the tool does and how to interpret model statuses. The only notable gap is the absence of explicit usage guidance relative to sibling tools.

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 has zero parameters, so per the scoring rules the baseline is 4. There is no parameter information to clarify or omit.

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 (List) and resource (the models llama-server offers) with the added qualifier 'with their status'. This is clearly distinct from sibling tools like chat, complete, tokenize, and health, though it does not explicitly name or contrast them.

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

Usage Guidelines2/5

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

The description explains what statuses mean but never says when to call this tool versus alternatives, nor does it describe prerequisites or follow-up actions. There is no explicit when-to-use or when-not-to-use 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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