predict_earnings

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

No annotations are provided, so the description carries full weight. It discloses the four input signals, the exact output fields (beat_probability_pct, signal, key_risks, what_to_watch, next_earnings_date), and mentions the 'AI earnings preview' nature. It doesn't discuss limitations or accuracy, but it is transparent about the inputs and outputs, which is substantial.

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 moderately lengthy but well-structured with bullet points for inputs, outputs, and an example use case. It is front-loaded with the core purpose and every section contributes value. The 'Viral use' sentence is slightly informal but still provides usage context, so it doesn't feel wasteful.

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 complexity (multiple signals, rich output), the description is comprehensive. It covers what the tool does, which signals are used, what it returns (with field names and examples), and the parameter. Even though an output schema exists, the description's return-type summary is useful and complete enough for an agent to 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 coverage is 0%, but the description compensates by documenting the single parameter: 'symbol: NSE symbol (e.g. TCS, INFY, HDFCBANK, RELIANCE)'. This adds examples and clarifies the expected format beyond the bare schema definition, which is helpful for an agent selecting the correct input.

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 the tool's purpose: 'AI earnings preview before quarterly results' that estimates beat/miss probability. It specifies a concrete action (predicting earnings) and a resource (upcoming quarterly reports), distinguishing it from sibling tools like nse_quarterly_results or earnings_calendar which report actuals or dates.

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 usage before quarterly results and gives a specific use case: 'post prediction before TCS/Infy results.' It does not explicitly mention alternatives or when-not-to-use, but the context is clear enough that an agent would know to invoke it when an earnings preview is requested prior to results.

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