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

With no annotations provided, the description carries the burden and does well: it discloses data sources (SEC XBRL, latest market close), the computation basis (TTM fundamentals), and that the response includes the computation convention. It lacks details on limitations or error conditions, but for a read-only valuation tool this is solid coverage.

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 long but information-dense, with each clause earning its place. It front-loads the core purpose, then lists sources, outputs, parameter usage, and an alternative endpoint. The use of dashes and parentheticals keeps related details together without fluff.

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 (multiple metrics, no output schema), the description is exceptionally complete. It enumerates all key outputs (multiples, derivations, margins, growth), explains the data sources, states the computation convention is included in the response, and clarifies parameter usage. This is sufficient for an agent to select and invoke the tool correctly.

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 descriptions are absent (0% coverage), so the description compensates by explaining the two parameters: 'symbol (any SEC-mapped ticker)' and 'companyName (resolves Nasdaq/NYSE listings)' and their mutual exclusivity ('exactly one'). This adds meaningful guidance beyond the raw 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: 'Get live valuation metrics for any US-listed company.' It specifies the verb (get), the resource (valuation metrics), and the scope (any US-listed company), and distinguishes from IPO-focused siblings by adding '(not just IPOs)' and noting it is used for peer benchmarking.

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 implies usage context by stating 'any US-listed company (not just IPOs)' and mentioning peer benchmarking, which suggests when to use it over IPO-specific tools. However, it does not explicitly name alternatives or state when not to use it, so it stops short of full guidance.

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