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 explains the core behavior (finding semantically related models) and provides an example, but it does not disclose details like what the response format will be, whether results are ordered, or how relationship types are determined beyond the schema. It is minimally adequate but lacks richer context.

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 two sentences plus a compact example. It is front-loaded with the main purpose, and every word contributes to understanding. No filler or redundancy.

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 has only two parameters and no output schema, the description is reasonably complete but still leaves the return value to inference. The example hints that model names are returned, but without an explicit statement of the output shape, the description is not fully complete for an agent that has never invoked the tool.

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 already provides 100% coverage with detailed descriptions for both parameters. The description adds a concrete example but no additional semantic meaning beyond what the schema already conveys, so the baseline of 3 is appropriate.

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 opens with a specific verb ('Find') and a clear resource ('Smart Data Models that are semantically related to a given model'). It distinguishes itself from sibling search tools by focusing on semantic relationships rather than generic search or attribute lookup, and the concrete example reinforces this unique purpose.

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?

It explicitly states the use case: 'useful for discovering adjacent models when building multi-entity systems.' This gives clear context for when to invoke the tool, though it does not explicitly mention alternative tools or say when not to use them, which prevents a 5.

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