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

With no annotations, the description carries the burden of behavioral disclosure. It explains that the tool aggregates specific sources and returns a signal plus conviction, which is meaningful. However, it does not describe the expected output format, scale, or any limitations such as data coverage or recency.

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, tightly worded sentence that front-loads the tool's purpose and immediately follows with concrete detail about inputs and outputs. There is no filler or redundant 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?

The tool is simple—one required parameter, no output schema—and the description adequately conveys what data is consumed and what is produced. It would be more complete if it specified the nature or range of the signal/conviction, but an agent can correctly invoke the tool with the provided information.

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 fully documents the single ticker parameter, including an example, so the description adds only marginal reinforcement by calling out 'US stock' in the prose. This aligns with the baseline of 3 for high schema coverage.

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 what the tool does: it aggregates multiple smart-money data sources into an accumulation/distribution signal with conviction, and scopes it to US stocks. It is distinguishable from generic signal tools by its explicit data-source list, though it does not name or contrast a specific sibling.

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 when to use it—when a composite smart-money view of a US stock is needed—but provides no explicit guidance on when not to use it or how it compares to alternatives like equity_signal. Usage context is inferable but not directly stated.

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