Sibyl

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 and does so effectively. It describes the return format (current price, trend, moving averages, etc.), source (Yahoo Finance), and includes practical examples of symbols across asset classes. It doesn't mention rate limits, authentication needs, or error handling, but provides substantial operational context beyond basic functionality.

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 appropriately sized and front-loaded: the first sentence states the core purpose, followed by return data, then parameter details with examples, and finally common symbol references. Every sentence adds value without redundancy, making it efficient and well-structured for quick comprehension.

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 moderate complexity (2 parameters), no annotations, and the presence of an output schema (which handles return values), the description is complete enough. It covers purpose, usage, parameters with examples, and behavioral context like data sources and symbol formats, leaving no significant gaps for effective tool invocation.

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?

With 0% schema description coverage, the description fully compensates by providing detailed parameter semantics. It explains 'symbols' as comma-separated ticker symbols with examples across markets (US, Canada, Crypto, Commodities) and 'period' as time periods with allowed values and default. This adds crucial meaning beyond the bare 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's purpose with specific verb ('Fetch') and resource ('real financial/stock/ETF data from Yahoo Finance'), distinguishing it from siblings like 'analyze', 'chart', or 'compare' that likely process rather than retrieve data. It explicitly mentions what data is returned (price, trend, moving averages, etc.), making the purpose unambiguous.

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 by specifying it fetches raw market data from Yahoo Finance, implying it's for data retrieval rather than analysis or visualization. However, it doesn't explicitly state when not to use it or name alternatives among siblings (e.g., 'chart' for visualization, 'analyze' for processing), leaving some ambiguity in tool selection.

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