show_valuation

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

No annotations are provided, so the description carries full burden. It discloses data sources (price cache for market cap/PE, XBRL filings for other data) and a prerequisite (Finnhub key). It doesn't specify error behavior, but the transparency is high for a simple tool.

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 three sentences with no wasted words. The first sentence lists the output metrics, the second gives usage triggers, and the third mentions data sources and prerequisites. It is front-loaded and efficient.

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 has one parameter, no output schema, and no annotations, the description adequately covers purpose, usage, data sources, and a prerequisite. It could be more complete by noting possible errors or return format, but it's sufficient for its simplicity.

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 100% (only one parameter with description). The description adds value by explaining that the ticker parameter is used to fetch data from multiple sources, going beyond the schema's simple description.

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 it performs a 'valuation deep-dive for a single ticker' and lists specific metrics (PEG ratio, Price/Sales, FCF yield). It distinguishes itself from sibling tools like show_earnings and show_financials by focusing on valuation.

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 explicitly provides usage examples: 'Use when the user asks is X overvalued / cheap?, what's the PEG?, how does the valuation look?'. This directly guides an AI agent on when to invoke the tool.

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