earnings_context

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

With no annotations, the description carries the full burden of behavioral disclosure. It does this well by noting 'Every figure is fetched, none derived,' which clarifies that the tool returns reported data rather than computed or modeled values. It also scopes the data to the last four quarters and next-quarter consensus, adding meaningful behavioral context beyond the schema.

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 tight sentences with no filler. The first sentence front-loads the full data scope, and the second sentence adds a meaningful behavioral guarantee. Every clause earns its place.

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 a single well-understood symbol parameter, no annotations, and no output schema, the description provides enough detail about the returned content to make the tool usable. It lists the major data categories but does not describe response format, units, or edge-case behavior, which would be desirable but are not critical for 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?

The schema has no parameter descriptions and 0% coverage, so the description should compensate. It adds useful domain context by saying 'One company's reported record,' which implies the symbol parameter should identify a company rather than a fund or index. However, it does not explain the expected format, case sensitivity, or accepted symbol types beyond that implication.

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 enumerates the resource: one company's reported earnings record spanning four quarters, including estimate/actual/surprise, revenue and net-income trend, and next-quarter consensus. It is specific and distinguishable in content, though it lacks an explicit verb and does not directly contrast itself with the similarly named sibling earnings_history.

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 explains what data is returned but gives no guidance on when to use this tool versus alternatives such as earnings_history or key_stats. There are no explicit conditions, exclusions, or named sibling comparisons, so an agent must infer appropriate usage.

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