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AI Guardian

AI Guardian

by AIops-tools
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Python
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model_details

Read-only

Inspect a specified Ollama model to retrieve its license, parameters, and capabilities for governance, auditing, and configuration checks.

Instructions

[READ] License / parameters / capabilities for one model.

Args: model: Model name (e.g. "llama3.2:3b"). 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": "model_details"
  }
}

Input Schema

NameRequiredDescriptionDefault
modelYes
targetNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.6.0

TDQS

B3.4/5.0
Behavior3/5

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

Annotations already declare readOnlyHint=true, and the description's '[READ]' tag merely echoes that. It does add value by naming what the call returns (license/parameters/capabilities), but says nothing about error behavior when a model is absent or how large/rich the payload is.

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 purpose in one bracketed line, then lists only the two parameters. Every sentence is useful, though the '[READ]' marker duplicates the annotation and the Args block could be folded together.

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?

For a simple two-parameter read tool with no output schema and a clear annotation, the description supplies purpose, returned content, and both parameter meanings. Adequate; only absent-model/error semantics are unaddressed.

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 coverage is 0%, so the description carries the full burden and mostly succeeds: it gives a concrete example format for 'model' ("llama3.2:3b") and explains 'target' as an Ollama target name with an explicit default when omitted. Minor gap: it doesn't say whether target accepts multiple entries or how names are discovered.

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 clear verb and resource — read details for a single model — and enumerates the returned content (license, parameters, capabilities). The 'one model' scope implicitly separates it from list_models/running_models, but no sibling is named explicitly.

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?

No statement of when to use this versus list_models, model_provenance, or running_models. Only the target parameter's default behavior is hinted at ('omit for the default'), which is parameter guidance, not usage 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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