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

Without annotations, the description carries the full burden of behavioral disclosure. It does not state whether the tool is read-only, what data source it uses, how 'best sector' is determined, or what the return format is. This is a significant gap for an analysis 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 a single sentence that front-loads the key metrics and ends with a clear action. Every word is necessary; there is no filler or redundancy.

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 annotations and output schema, and the presence of many sibling analysis tools, this description is too sparse. It does not explain input constraints, output structure, or how it differs from alternatives, leaving the agent under-informed for reliable 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?

Schema description coverage is 0%, and the description does not explain the 'market' or 'timeframe' parameters. The default values give some hints, but the agent is left to guess valid values such as country codes or timeframe formats. The description adds no semantic meaning beyond the parameter names.

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 analyzes sector-level metrics (return, risk, momentum, drawdown, correlation) and selects the best sector. It distinguishes itself from single-metric siblings like analyze_momentum and analyze_statistics by covering multiple dimensions plus a selection action.

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?

No explicit guidance is provided about when to use this tool versus sibling analyzers (e.g., analyze_momentum, analyze_trend). There are no exclusions, prerequisites, or alternative suggestions, leaving the agent to infer usage solely from the purpose.

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