detect_earnings_quality

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

With no annotations, the description carries full responsibility for behavioral disclosure. It specifies the output format (0-100 score plus flag list), explains what the score means (share of clean checks), and adds an important interpretive caveat that findings are observations, not proof of a problem. It does not explicitly state read-only behavior, but the analytical nature and output description make that reasonably clear.

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 efficiently front-loaded with the core action, followed by a scannable list of detection criteria, output summary, and caveat. Each sentence adds useful content; the param line is clearly separated. It is slightly longer than necessary but every portion serves a purpose.

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 one-parameter analysis tool with an output schema, the description is largely complete: it explains inputs, detection logic, output semantics, and the interpretive stance of the results. The main gap is lack of differentiation from sibling analysis tools, and it does not mention any data-freshness or coverage limitations, but these do not prevent correct 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 provides only a required string field named 'symbol' with no description, so the description must compensate. It does this well by defining the parameter as a ticker symbol and providing concrete examples: RELIANCE, TCS.NS, TCS.BO. This gives the agent enough format context to construct a valid call, though it does not exhaustively specify all exchange-suffix rules.

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?

Starts with a specific verb and resource: "Flag earnings-quality concerns for an NSE/BSE-listed Indian equity." The description then enumerates the exact checks performed (operating cash flow versus profit, receivables/inventory growth, other income, cash-flow backing), clearly distinguishing it from broad ratio or health-score siblings. This leaves no ambiguity about what the tool does.

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 appropriate use by defining the tool's scope and output, but it does not explicitly state when to prefer this tool over closely related siblings like detect_accounting_red_flags or analyze_cash_flow_quality. There is no exclusion guidance, so an agent must infer the differentiation from purpose alone.

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