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

With no annotations provided, the description carries the full burden and compensates well by disclosing the exact output components ('trigger context, predictor value, direction, narrative paragraph') and the intended use for reports and briefings. It doesn't cover potential limitations or data freshness, but the absence of side effects for a read-only tool is implicitly clear.

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 four sentences, each adding meaningful detail: what it does, what narratives include, target audience, and output components. It is front-loaded with the primary verb and remains focused without unnecessary fluff.

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 simplicity (no parameters, no output schema), the description sufficiently explains the return values and audience. It clearly states that it produces a narrative paragraph with specific fields, which is adequate for a no-arg read tool. Minor gaps like how 'active' is determined or whether results are sortable are not critical for basic usage.

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 tool has zero parameters, so the baseline is 4. The description adds contextual meaning by mentioning 'active predictive signals' which implies the scope of the data returned, even though there is no input schema to elaborate.

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 uses a specific verb ('Get') with a clear resource ('plain-language explanations of active predictive signals'). It explicitly describes what each narrative contains (mechanism, economic logic, current reading) and distinguishes from sibling tools like get_predictive_signals by emphasizing the non-technical, explanatory nature.

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 clearly targets non-quantitative users and explains the purpose ('understand the why... without reading F-statistics'). This gives clear context for when to use the tool, though it does not explicitly name alternative tools or state when not to use it.

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