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model_pull

Download AI models with progress updates. Choose native HuggingFace GGUF or remote sources, and optionally allow unsupported architectures.

Instructions

Download a model, streaming progress via MCP notifications.

Args:
    model: Model ref to pull (e.g. "Qwen/Qwen3-0.6B-GGUF" or
        "Qwen/Qwen3-0.6B-GGUF/Qwen3-0.6B-Q8_0.gguf").
    source: "native" (HuggingFace GGUF) or "remote" (SDK-managed).
    allow_unsupported: Set true to pull even when the model's architecture
        isn't supported by this lilbee build. Default refuses with a
        structured error and the list of supported architectures.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYes
sourceNonative
allow_unsupportedNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior4/5

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

No annotations provided, so description carries burden. Discloses streaming progress and behavior of allow_unsupported (structured error with supported architectures). Does not mention overwrite behavior or authentication requirements.

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?

Well-structured with a summary line followed by parameter explanations. Could be slightly more concise by integrating parameter info into the main description, but it is clear and front-loaded.

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?

Covers key behavioral aspects (streaming, error handling) and parameter details. Output schema exists, so return value explanation is not needed. Lacks information on potential side effects like overwriting existing models or network dependency.

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

Parameters5/5

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

Schema coverage is 0%, so description must fully explain parameters. It does so with examples for 'model', options for 'source', and detailed explanation of 'allow_unsupported' including default behavior and error type.

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?

Description clearly states 'Download a model' with specific verb and resource, and streaming progress notification. Distinguishes from siblings like model_list, model_rm, model_show by focusing on download action.

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

Usage Guidelines4/5

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

Provides clear context for when to use (downloading models), including source options ('native' vs 'remote'). However, lacks explicit guidance on when not to use or comparison with alternatives like model_list or model_rm.

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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