Get financial history

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

The annotations include readOnlyHint: true, so the description doesn't need to reiterate that. It adds behavioral context by describing the returned structure (groups like income statement, balance sheet, etc.) and mentioning consistency checks. This goes beyond what annotations provide.

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 a single, well-structured paragraph that front-loads the key action and details. Every sentence adds value: it states the fetch range, return types, data groups, and use cases. No wasted words.

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 absence of an output schema, the description compensates by listing the return data groups (income statement, balance sheet, etc.) and mentioning specific use cases. It provides a good sense of what the tool returns, though a bit more detail on the exact output format could improve completeness.

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%, so parameters are well documented. The description adds meaningful context beyond the schema, such as specifying that tickers must be exact Bullrun tickers, explaining that annual rows use fiscalQuarter=0, and describing the nature of includeEmptyRows. This enriches the parameter understanding.

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 fetches historical financial statements for a Bullrun ticker, specifying the range (1-15 years), return types (annual/quarterly), and data groups (income statement, etc.). It distinguishes well from sibling tools like get_forward_estimates or get_quality_moat_metrics.

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 says 'Use this when evaluating multi-year revenue/net-income growth, margin trajectories, leverage, cash flow quality, or whether a stock passed a rule such as 10% revenue and net-income growth every year.' This provides strong usage context, though it doesn't explicitly state when not to use it or name alternative tools for different purposes.

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