Compare AI Employee Opportunities

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

Annotations already declare readOnlyHint=true, idempotentHint=true, and destructiveHint=false. The description adds valuable behavioral context beyond this: it guarantees no paid source runs and explains that missing research will be reported explicitly. This enriches the agent's understanding without contradicting the 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?

The description consists of two crisp, front-loaded sentences. The first sentence states the core function, and the second provides a crucial behavioral boundary. Every word adds value with zero 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's moderate complexity (2 parameters, one required), the description covers the main purpose, the no-cost guarantee, and the missing-data behavior. It lacks any description of the return value format, but the explicit statement about missing research reporting partially compensates. The rich annotations also reduce the overall burden.

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 description coverage is 0%, so the description must compensate for parameter explanation. It only hints at category_ids via 'two or more AI employee categories' but completely ignores the 'markets' parameter and does not explain its purpose, allowed values, or default behavior. The schema provides no description either, leaving the agent guessing.

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 clearly states the tool's function: 'Compare two or more AI employee categories using stored evidence.' It uses a specific verb (compare) and specific resource (AI employee categories). The second sentence differentiates it from tools that start paid research, distinguishing it from siblings like research_ai_employee_market.

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 provides clear usage context: it compares categories using stored evidence and explicitly states that it 'Does not start paid source runs,' indicating when not to use it. However, it does not explicitly name sibling alternative tools or provide a full when-to-use vs. when-not-to-use matrix.

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