predict

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

With no annotations, the description carries the behavioral burden and does disclose meaningful traits: predictions include intervals, are scaled by real-vs-simulated results, and are labelled with their calibration basis. It also explains that targets yield a probability of success and that an adoption forecast is produced, though it does not explicitly state read-only behavior or permission requirements.

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 dense sentences that front-load the main prediction purpose and then add output details. It is information-rich with little waste, though the semicolon-heavy structure could be easier to scan.

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 complex tool with no output schema and no annotations, the description explains the kinds of predictions and their calibration labels reasonably well. It remains incomplete for the input parameters days, brand, and spend, and does not describe the return shape beyond naming metrics.

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 five parameters. It clarifies run and targets, including a targets example, but does not explain days, brand, or spend, leaving most parameters without semantic meaning.

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 names a specific verb, predict, and enumerates the resources/metrics covered: CTR per ad, cost per lead, conversion, email click rates, and adoption forecast. It is clear what the tool does, though it does not explicitly differentiate itself from siblings such as results or study.

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 implies the tool should be used after backtest and calibrate by referencing their stored results as inputs, and it notes optional targets for probability of success. However, it gives no explicit when-to-use guidance versus siblings and no exclusions.

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