show_valuation

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

With no annotations, the description fully bears the burden of behavioral disclosure. It explains the tool uses price cache for market cap/PE and XBRL filings for other data, requires a Finnhub key for authentication, and computes metrics from 8 quarters of filings. No contradictions.

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 concise with three efficient sentences: purpose, use cases, and requirements/data sources. Front-loaded with key information, every sentence adds value without redundancy.

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 single parameter and lack of output schema, the description is complete. It covers the tool's function, when to use it, prerequisites, and data provenance, leaving no essential gaps for an AI agent.

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% with one parameter 'ticker' already well-described in the schema. The description does not add significant new semantic detail beyond what the schema provides, so baseline 3 is appropriate.

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 performs a valuation deep-dive for a single ticker, listing specific metrics (PEG ratio, Price/Sales, FCF yield) and data sources. It distinguishes itself from sibling tools like show_earnings or 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 advises when to use the tool with example user queries ('is X overvalued / cheap?', 'what's the PEG?') and mentions a prerequisite (Finnhub key). However, it does not provide explicit when-not-to-use guidance or mention alternatives among siblings.

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