get_option_price

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It effectively describes key behaviors: it's a read operation (implied by 'Get'), uses historical data up to 5 days back, throws errors for missing data or contracts, and returns specific fields. It could improve by mentioning rate limits or authentication needs, but covers core operational aspects well.

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 well-structured and appropriately sized. It front-loads the purpose, explains the process, lists return values, and details parameters—all in clear, efficient sentences with zero waste. Every sentence adds value, such as clarifying data freshness or error conditions.

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 (ML-based prediction with 4 parameters) and the presence of an output schema (implied by the detailed return fields), the description is complete. It covers purpose, process, return values, parameters, and error conditions, providing all necessary context for an AI agent to use the tool effectively.

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 0%, so the description must compensate. It adds significant meaning beyond the schema: explains what each parameter represents (e.g., 'Ticker symbol', 'Strike price as a number'), provides examples (e.g., 'AAPL', 150.0), and specifies constraints (e.g., date format 'YYYY-MM-DD', option_type must be 'call' or 'put'). This fully documents all 4 parameters.

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: 'Get Helium's proprietary ML model-predicted price for a specific option contract.' It specifies the verb ('Get'), resource ('price'), and method ('Helium's proprietary ML model-predicted'), distinguishing it from siblings that focus on biases, news, or tickers.

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 clear context for when to use this tool: to obtain a predicted fair-value price for a specific option contract using Helium's ML model. It mentions prerequisites (options chain data availability within 5 days) but does not explicitly state when not to use it or name alternatives among siblings.

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