model_predict

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

Annotations already indicate readOnlyHint=true and openWorldHint=true, so the tool is known to be a safe read operation. The description adds the behavioral detail that local file paths are not supported, which is valuable beyond annotations. It does not contradict annotations.

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 that includes the core action, the resource, and a crucial constraint. Every word is necessary and earns its place. No wasted text.

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 6 parameters, 2 required, and no output schema, the description is far too minimal. It does not explain what the tool returns, how to handle results, or what constitutes a successful inference. The agent would lack crucial context for integration.

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?

Schema coverage is only 33% (2 of 6 parameters have descriptions). The description does not explain the meaning or usage of iou, conf, imgsz, or project. It barely adds value beyond the schema's minimal descriptions. For a low-coverage schema, the description should compensate but fails to do so.

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?

Description clearly states the verb 'Run inference' and resource 'trained model on an image URL or base64 source'. It also explicitly excludes local file paths, distinguishing it from any possible file-based siblings. This verb+resource+scope combination is specific and unambiguous.

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 when to use (when inference is needed) and provides a constraint (no local file paths), but does not explicitly state when not to use or suggest alternative tools. It gives clear context but lacks exclusions or comparisons.

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