veroq_sectors

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

With no annotations provided, the description discloses behavioral traits such as cost (1 credit) and return structure (per-sector data including ticker count, brief count, average sentiment, top tickers). This goes beyond the input schema but could mention data freshness or limitations.

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 concise with labeled sections (WHEN TO USE, RETURNS, COST, EXAMPLE) that front-load key information. Every sentence is useful and there is no redundant text.

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 simple tool with one parameter and no output schema, the description provides sufficient context: purpose, use case, return fields, and cost. It is complete enough for an AI agent to select and invoke correctly, though it could clarify sentiment scale.

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% with one parameter 'days' described as 'Lookback period in days (default 7)'. The description adds an example usage but no additional semantic detail beyond the schema, so baseline of 3 is appropriate.

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 verb 'Get', resource 'sector overview', and includes specific outputs like aggregate sentiment scores and brief counts. It distinguishes itself from sibling tools that focus on individual tickers or other macro data.

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 includes a 'WHEN TO USE' section that explicitly states the use case: macro view for sector rotation analysis. It provides clear context but does not explicitly list when not to use or name alternative tools.

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