list_fraud_agents

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

There are no annotations, so the description needs to convey safety and side effects. It states it returns a list of 10 specialists, which implies a read-only operation, but it doesn't explicitly say it doesn't modify anything. It also doesn't clarify if the list is cached or if it takes time, but since it's a simple list with no params, the behavior is fairly predictable. The description adds some value by noting the shared data model, which hints at the underlying structure.

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 a single, concise sentence that front-loads the action ('List the 10 Databricks Genie fraud specialists') and adds a relevant detail about their data model. There is no fluff; every word contributes to the meaning. This is an exemplary model of brevity.

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 that there are no parameters and no annotations, the description is nearly complete: it explains what the tool does and provides a key detail about the output (the 10 specialists share OLTP + dims/facts). However, it doesn't describe the output schema (which exists), but since the description doesn't need to repeat what the output schema already holds, this is not a major gap. The description is adequate for a simple listing 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 input schema has zero parameters, and the description confirms that by mentioning 'the 10'—the list size is fixed. Since there are no parameters, there's no additional semantic burden; the description fully covers the empty schema. This is a case where the description doesn't need to explain anything, so a high score is appropriate. However, it could be clearer that the list is fixed and no parameters are needed.

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 tells the agent exactly what this tool does: it lists the 10 Databricks Genie fraud specialists, which is a specific, concrete action. It includes the key detail that these specialists share a specific data model (OLTP + dims/facts), which adds useful context. However, it doesn't explicitly differentiate from sibling list_fraud_cases, though the nouns differ (agents vs cases), so the differentiation is implicit.

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 the use case—when you need an overview of the fraud agents available—but it doesn't explicitly say when to use this instead of list_fraud_cases or run_fraud_agent_cases. The tool has no parameters, so it's a simple listing operation, but no guidance is given on what actions to take with the results.

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