Download AI models locally with live progress. Choose native GGUF or remote SDK sources, and override architecture restrictions when needed.
Download a model and stream progress.
source is native (GGUF) or remote (SDK).
allow_unsupported overrides the supported-architecture refusal.
| Name | Required | Description | Default |
|---|---|---|---|
| model | Yes | ||
| source | No | ||
| allow_unsupported | No |
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
Changes observed during successful MCP inspections.
v1.0.2Input schema / properties / allow_unsupported / defaultRemoved value: -falseInput schema / properties / allow_unsupported / titleRemoved value: -"Allow Unsupported"Input schema / properties / model / titleRemoved value: -"Model"Input schema / properties / source / defaultRemoved value: -"native"Input schema / properties / source / titleRemoved value: -"Source"Input schema / titleRemoved value: -"model_pullArguments"v1.0.0Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description must fully disclose behavior. It mentions streaming progress and parameter effects but omits important details like auth requirements, side effects, or error handling. For a download tool, this is insufficient.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
Extremely concise: two lines with no redundant information. The first sentence states the core purpose, followed by precise parameter explanations. Every sentence earns its place.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given the complexity of a download tool with streaming and no annotations, the description covers basic usage and parameter meanings. However, it lacks details on return format (though output schema exists), error scenarios, or concurrency/rate limits. Adequate but with clear gaps.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema description coverage is 0%, so the description compensates by explaining source as 'native (GGUF) or remote (SDK)' and allow_unsupported as 'overrides the supported-architecture refusal.' Model parameter is not elaborated, but overall it adds significant meaning beyond the schema types.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states 'Download a model and stream progress,' which is a specific verb-resource pair. It distinguishes from siblings like model_list and model_show by focusing on downloading rather than listing or inspecting.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
No guidance is provided on when to use this tool over alternatives such as model_show or search. The description only explains parameter details but lacks context like prerequisites or use cases.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.