get_dupont_data

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

No annotations are provided, so the description carries the full burden of behavioral disclosure. It only states what data is provided without describing how it behaves—e.g., whether it's a read-only query, if it requires authentication, rate limits, error handling, or what the output looks like (since no output schema exists). This leaves significant gaps for an agent to understand the tool's operation.

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 extremely concise with a single phrase 'Quarterly Dupont analysis data.', which is front-loaded and wastes no words. However, this conciseness comes at the cost of completeness, but as per the dimension, it scores high for being brief and structured.

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 complexity (5 parameters, 3 required, no annotations, no output schema), the description is incomplete. It doesn't explain the tool's behavior, parameters, or output, leaving the agent with insufficient information to use it effectively. The lack of annotations and output schema increases the burden on the description, which it doesn't meet.

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 description coverage is 0%, meaning none of the 5 parameters (code, year, quarter, limit, format) are explained in the schema. The description adds no parameter semantics—it doesn't clarify what 'code' refers to (e.g., stock ticker), the format of 'year' and 'quarter', or what 'format' options exist. This fails to compensate for the low schema coverage.

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 'Quarterly Dupont analysis data' states what the tool provides (Dupont analysis data) and indicates temporal scope (quarterly), which gives a basic purpose. However, it lacks a specific verb (e.g., 'retrieve' or 'fetch') and doesn't differentiate from siblings like 'get_fina_indicator' or 'get_profit_data', which might also provide financial metrics, making it somewhat vague.

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 provides no guidance on when to use this tool versus alternatives. It doesn't mention any context, prerequisites, or exclusions, and with many sibling tools for financial data (e.g., 'get_balance_data', 'get_cash_flow_data'), there's no indication of how Dupont analysis differs or when it's preferred.

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