predict_earnings

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

No annotations exist, so the description carries full burden. It discloses the four signals used, the outputs (beat_probability_pct, signal, key_risks, etc.), and the data source (yFinance). Missing limitations or reliability notes, but overall transparent.

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 logically structured (purpose, signals, returns, viral use, args) and mostly concise. The 'viral use' section adds length but is not essential, slightly reducing conciseness.

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 (predictive model with 4 signals) and an output schema that details returns, the description adequately covers purpose, inputs, and outputs. It lacks explanation of the model's reliability or calculation method, but remains fairly complete.

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 input schema has only one parameter 'symbol' with 0% description coverage. The description compensates by stating 'NSE symbol' and providing examples (TCS, INFY, etc.), adding meaning 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 clearly states the tool predicts earnings outcomes before quarterly results using a specific verb ('predict') and resource ('earnings'). It distinguishes itself from sibling tools like 'earnings_calendar' and 'nse_quarterly_results' by being a predictive AI model, not just data retrieval.

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 provides explicit usage context (e.g., 'Viral use: post prediction before TCS/Infy results') and explains it combines 4 signals, implying when it is useful. However, it does not explicitly state when not to use it or compare it to alternative tools like 'earnings_calendar'.

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