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model_pull

Download AI models and stream progress from native GGUF or remote SDK sources, with an override for unsupported architectures.

Instructions

Download a model and stream progress.

source is native (GGUF) or remote (SDK). allow_unsupported overrides the architecture refusal.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "model": "<model>"
    },
    "name": "model_pull"
  }
}

Input Schema

Name	Required	Description	Default
`model`	Yes
`source`	No
`allow_unsupported`	No

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

Changed6 schema fields changedv1.0.2Jul 22, 2026
- removedInput schema / properties / allow_unsupported / default
```
Removed value: -false
```
- removedInput schema / properties / allow_unsupported / title
```
Removed value: -"Allow Unsupported"
```
- removedInput schema / properties / model / title
```
Removed value: -"Model"
```
- removedInput schema / properties / source / default
```
Removed value: -"native"
```
- removedInput schema / properties / source / title
```
Removed value: -"Source"
```
- removedInput schema / title
```
Removed value: -"model_pullArguments"
```
First observedv1.0.0Jun 15, 2026

TDQS

B3.4/5.0

Behavior3/5

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

With no annotations, the description carries the full behavioral burden. It usefully discloses progress streaming, the meaning of source, and that allow_unsupported overrides an architecture refusal (a notable safety bypass), but omits auth/permission requirements, error behavior, and where the download lands.

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?

The core action is front-loaded in the first sentence, followed by two terse parameter notes. Nearly every clause earns its place, though the doubled backticks are a formatting artifact.

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?

An output schema exists so return values need not be described, and the source/override semantics are covered. However, for a mutation-style download with 0% schema coverage and no annotations, missing prerequisites and failure behavior leave it only adequate.

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

Parameters3/5

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. It meaningfully explains two of three parameters (source native=GGUF/remote=SDK, allow_unsupported override), but the required model parameter receives no explanation at all.

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 ("Download a model") plus a secondary behavior ("stream progress"). It is clearly distinguishable from siblings like model_show (inspect), model_rm (remove), and model_list (enumerate).

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?

There is no explicit when-to-use or when-not-to-use guidance and no routing to alternatives. The source and allow_unsupported notes hint at configuration choices, but the agent is left to infer the appropriate context for invoking a pull.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.