AI Guardian

by AIops-tools

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Python

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pull_model

Destructive

Pull an Ollama model while enforcing allow/deny policies, refusing requests that violate governance rules.

Instructions

[WRITE][risk=medium] Pull a model — refused if it violates the deny/allow policy.

Args: model: Model name to pull. target: Ollama target name from config; omit for the default.

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": "pull_model"
  }
}

Input Schema

Name	Required	Description	Default
`model`	Yes
`target`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedv0.6.0Jul 29, 2026

TDQS

A3.7/5.0

Behavior4/5

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

Annotations already declare the destructive, non-idempotent, non-readonly profile, so the safety bar is lowered. The description adds genuine value beyond that by disclosing the deny/allow policy gate that can refuse the operation, which is not captured in annotations.

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?

Front-loads the operation type and risk tag, then the one-line purpose, then the args. The args section is justified by the 0% schema coverage, and there is no padding.

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 medium-risk write operation it covers the key gaps (the policy gate and both parameters) while annotations handle the safety profile, but it says nothing about the nature of the pull (registry download, potential size/time cost, or failure behavior). Adequate but leaves meaningful operational questions open.

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?

Schema description coverage is 0%, so the description must carry the param burden, and it does: it defines 'model' and explains that 'target' is an Ollama config name that should be omitted for the default. This compensates well for the empty schema descriptions.

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 and resource ('Pull a model') and tags the operation type and risk level via '[WRITE][risk=medium]'. This clearly separates it from read-oriented siblings like list_models and model_details, though it does not name a specific sibling to contrast with.

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?

Adds one useful condition ('refused if it violates the deny/allow policy') that tells the agent when the call fails, but gives no explicit guidance on when to pull versus unload/remove/pin operations. Usage is largely inferred from the purpose statement.

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