get_metrics

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

With no annotations, the description carries full disclosure burden. It reveals methodology ('Computed from SEC filings rather than estimated'), notes a caveat ('on average balances where available'), and discloses output transparency ('Each metric lists the line items it was computed from and the period it covers'). It does not mention rate limits, error behavior, or update frequency, but these are less critical for a read-only metrics 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 well-organized: a one-line summary, a methodology paragraph, a line about output structure, an explicit usage preference, and a concise Args list. Every sentence provides distinct value, with no fluff or repetition. Front-loading with the core purpose makes it easy to scan.

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?

The tool has moderate complexity (2 params, no nested objects) and an output schema, so the description need not detail return values. It covers data source, metric list, methodology caveats, output transparency, and sibling differentiation. This is complete 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?

The schema has 0% description coverage, but the description's Args section compensates fully. It explains 'ticker' as a stock symbol with an example, and clarifies 'history_years' as 'Periods to span when computing growth rates,' which adds meaning beyond the schema's name and default. This is high-value parameter documentation.

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 opens with a specific verb+resource: 'Derived financial metrics for a company' and enumerates exact metric categories (margins, returns, growth, leverage). It clearly distinguishes from siblings by stating 'Prefer this over deriving ratios yourself from get_financials,' naming the alternative tool.

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 tells the agent when to use this tool: whenever derived ratios are needed, and when not to use it (don't derive ratios yourself from get_financials). It also states the data source (SEC filings rather than estimated), giving context for trustworthiness. This is clear usage guidance with a named alternative.

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