get_model_info

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

No annotations are provided, so the description carries the full behavioral burden, and it discloses nothing about read-only safety, required connection state, or permissions. Since it is a zero-parameter read tool with an output schema, the omission is less costly, but the description adds no behavioral context at all.

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?

A single front-loaded sentence with the key categories listed immediately after the colon; nothing is wasted. It is arguably too terse to be fully helpful, but strictly on conciseness it is efficient.

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?

For a zero-param read tool with an output schema present, the description is minimally adequate: return values need not be explained, but with no annotations the safety profile and connection prerequisites remain undisclosed. Left a gap an agent would want closed before invoking.

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 tool takes zero parameters, so there is nothing for the description to clarify beyond the schema. Baseline 4 applies.

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?

States a clear verb (Get) and resource (model info) and enumerates the returned categories (bodies, named selections, analyses). It is distinguishable from sibling mutators, though it overlaps with list_named_selections and list_materials without explaining the difference.

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 on when to call this versus alternatives such as list_named_selections, list_materials, or get_mesh_statistics. The agent must infer its role as a general model-state inspection tool.

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