get_sector_breadth

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

No annotations are present, so the description carries the full burden of behavioral disclosure. It does clarify the core computation of the metric (buy-signal percentage), which is valuable. However, it discloses nothing about data recency, whether results are current or historical, or any limitations, leaving a noticeable gap.

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 efficient sentence, front-loaded with the metric name, followed by a concise parenthetical definition. No words are wasted and the structure makes the meaning immediately accessible.

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 parameterless tool with an output schema, the description adequately explains what the tool returns and how the metric is computed. It does not specify whether results cover all sectors in a given list or any period, but given the simplicity of the tool, the information provided is largely sufficient.

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 tool has zero parameters, so the schema coverage is 100% and there is no parameter documentation burden. The baseline of 4 applies because there is nothing the description needs to add about parameters.

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 states the exact resource and metric: bullish-percent breadth by sector, defined as the percentage of names on a Point & Figure buy signal. This is specific enough to distinguish it from siblings like get_pnf or get_macro, and an agent can infer exactly what data is returned.

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 gives no guidance on when to use this tool versus alternatives. It does not name any sibling tool, onboarding conditions, or exclusions. Because the description solely defines the metric, the usage context is left entirely to the agent's inference.

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