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

Beyond the annotations, the description discloses important behavioral traits: the experimental nature, the meaning and limitations of `lift` and `base_rate`, the resolution dates of forward cohorts, the restriction on presenting to end users, and the cost of 1 unit. This is substantial added transparency.

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 front-loaded with the experimental forecast purpose and then packs necessary caveats into three concise sentences. Every sentence adds distinct value, and there is no redundant or filler content.

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?

While the caveats are thorough, the tool has no output schema and the description only partly describes return fields (`lift`, `base_rate`). It also fails to clarify the parameters or how the agent should select a company, so the definition is not fully self-sufficient for correct invocation.

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%, and the description does not explain `domain` or `org_id` at all. It mentions 'a company' generally but never connects that to the parameters, leaving the agent without any parameter-level guidance beyond raw field names.

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 produces an experimental forecast of a company's potential next action (raise, be acquired, acquire), which is a specific verb and resource. It does not explicitly differentiate from sibling tools, but the experimental forecast framing makes the core purpose unmistakable.

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 usage as a ranking aid and warns against presenting results to end users, which constitutes implicit usage guidance. However, it does not state when to choose this tool over siblings like why_now or events_for_icp, nor does it mention any alternatives or exclusions.

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