earnings_calendar

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

Discloses that it returns earnings dates and analyst estimates (EPS and revenue). However, with no annotations, it does not mention any behavioral traits such as data source, rate limits, or scope (e.g., which exchanges). Adequate but could add context.

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?

Concisely structured with two paragraphs plus Args and Examples sections. The examples are helpful but slightly verbose; otherwise no wasted text.

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 that an output schema exists, the description need not detail return values. It adequately explains the purpose and primary output (earnings dates and estimates) for a simple lookup tool.

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 provides 0% description coverage for the 'symbol' parameter. The description compensates by stating it is a stock ticker with examples (RELIANCE, AAPL) and noting it is required, adding significant meaning.

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') and resource ('upcoming earnings dates') and clearly distinguishes from sibling tools like predict_earnings and nse_quarterly_results by focusing on expected upcoming reports.

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?

Provides examples of usage but no explicit when-to-use or when-not-to-use guidance relative to sibling tools. Implies usage from examples but does not exclude alternatives like predict_earnings for forward-looking estimates.

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