search_models

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

With no annotations, the description carries the burden of behavioral transparency. It explains returns but doesn't disclose that the tool is read-only or non-destructive. The schema hints at pagination and sorting, but the description omits explicit safety or side-effect information.

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?

Two sentences with no wasted words. The purpose is front-loaded, and every sentence adds value. Efficient and clear.

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 search tool with 6 optional parameters and no output schema, the description covers the core functionality. Missing details on pagination behavior, result limits, or error handling, but the schema provides parameter constraints. Sufficient for basic usage.

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 100%, so parameters are already documented in the input schema. The description adds minimal extra meaning beyond stating the tool's high-level function. It doesn't elaborate on parameter usage or provide examples not already in the 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 explicitly states it searches and browses AI models, returns specific fields (names, slugs, descriptions, categories), and advises use before running. It clearly distinguishes from siblings like run_model, recommend_model, and explore.

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 says 'Use this to discover available models before running them,' providing clear usage context. It doesn't explicitly state when not to use, but the purpose is straightforward and differentiated from other tools.

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