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

Annotations already declare read-only, idempotent, non-destructive, open-world behavior, so the description does not need to restate safety. It adds useful behavioral context by disclosing that the dataset is EPS-only, that revenue is not included, and what fields are returned per quarter.

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 definition is front-loaded with purpose, then return fields, then an exclusion and alternative. It is short, specific, and every sentence adds useful information without redundancy.

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?

There is no output schema, so the description appropriately explains the returned fields. Combined with fully described parameters and annotations covering safety, it gives the agent everything needed to select and invoke the tool correctly.

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 100%, so symbol, limit, and _apiKey are already fully documented in the schema. The description provides an example using symbol and limit but does not add material parameter semantics beyond the schema.

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 states a specific resource and metric: EPS actual vs analyst consensus, beat/miss, for recent quarters. It also names the exact per-quarter fields returned, so the agent can distinguish it from general earnings or calendar tools.

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 clearly scopes the tool to historical EPS surprise data and explicitly excludes revenue, directing revenue-estimate use cases to get_earnings_calendar. That gives both a when-to-use and a when-to-use-an-alternative condition.

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