Sector Performance

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains the aggregation behavior, the types of statistics returned, and the partial-name matching feature, including a concrete example. It does not explicitly state that the operation is read-only, but the phrasing and focus on historical trading data 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 concise and well-structured: an attention-grabbing example first, followed by the aggregation details, then the matching behavior. Every sentence adds value, and there is no redundant or filler content. The structure front-loads the core purpose before giving supporting detail.

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?

The description covers the tool's purpose, output statistics, and input matching behavior in a way that is sufficient for most calls. The presence of an output schema reduces the need to document return values in detail. It might have mentioned using list_sectors to discover valid sector names, but the partial-match behavior makes this less critical.

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 input schema provides only a bare string parameter with no description, so the description must compensate. It adds meaningful semantics by explaining that the parameter is a sector name and that partial matches are accepted, with an example. It could be even more explicit about acceptable formats, such as case sensitivity or exact name requirements, but the example provides enough guidance for correct invocation.

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 identifies the tool as aggregating sector-level trading statistics for a single DSE sector, listing specific outputs: advance/decline/never-traded counts, average move, and best/worst performers. The opening example ('did banks rise while textiles fell?') makes the purpose intuitive and distinguishes it from sibling tools like top_movers or get_stock, which focus on individual securities or market-wide moves.

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 clear context for when to use the tool: when the user wants to know how one sector traded today. It implies the contrast with market-wide or stock-specific tools, though it does not explicitly name alternatives or state when not to use it. The partial-name matching note also guides callers on what input format is acceptable.

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