get_model_info_tool

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behavioral traits: it returns detailed model information, distinguishes between configured models (exact values) and unknown models (estimates), and specifies the return format. This covers the tool's functionality and output behavior well, though it doesn't mention potential errors or rate limits.

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?

The description is appropriately sized and front-loaded, with the core purpose stated first, followed by key behavioral details, and then structured sections for Args and Returns. Every sentence earns its place by adding essential information without redundancy or fluff.

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 tool's moderate complexity (single parameter, informational purpose), no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, usage context, parameter semantics, and behavioral traits, providing all necessary context for an AI agent to use the tool effectively.

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?

The schema description coverage is 0%, so the description must compensate fully. It does this excellently by clearly explaining the single parameter 'model_name', providing its purpose ('Name of the model to get info for') and concrete examples ('e.g., "qwen2.5:14b", "llama3.1:70b"'), adding significant meaning beyond the bare schema.

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 the tool's purpose with a specific verb ('Get detailed information') and resource ('about a specific model'), distinguishing it from siblings like 'models' (likely listing models) or 'switch_model' (changing models). It explicitly identifies what information will be retrieved: VRAM requirements, context window size, and tier classification.

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?

The description implies usage by specifying it's for getting information about 'a specific model', suggesting it should be used when detailed model specs are needed. However, it doesn't explicitly state when to use this tool versus alternatives like 'models' (which might list available models) or provide clear exclusions or prerequisites for usage.

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