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

Annotations already indicate read-only and non-destructive, and the description adds valuable behavioral context: reverse-DCF methodology, SEC EDGAR data source, coverage of about 2,000 US tickers, and what it intentionally does not return. No contradiction with annotations; the added context goes beyond the schema.

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 front-loaded with the core purpose, followed by exclusions and scope, all in three sentences. It is concise, though phrases like 'boothcheck' and 'where the route holds one as an input' are slightly confusing and could be cleaned up for clarity.

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?

For a reverse-DCF tool with no output schema, the description covers key output dimensions (growth, years, terminal margin), data source, and coverage. It does not specify exact return format or behavior for uncovered tickers, but the core context is sufficiently complete for selection and 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?

The schema fully describes the single ticker parameter, but the description adds meaning by specifying US stocks, coverage scope (~2,000 tickers), and that the analysis is based on current price. This helps the user understand ticker validity and potential coverage limitations.

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 inverts a US stock's current price into implied operating-income growth, years sustained, and terminal margin, versus actual delivered results. This is a specific, unique purpose that distinguishes it from siblings like compare_priced_in and most_stretched, which likely focus on comparison and ranking.

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?

It provides clear context for when to use the tool: to judge the bet a price implies, and explicitly lists exclusions (no fair value, price target, or rating). However, it does not explicitly name alternative tools or state when to prefer a sibling, so it lacks full alternative guidance.

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