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

With no annotations, the description carries the behavioral disclosure burden; it discloses that results are computed from live derivatives data across 13 venues and that the response includes liquidation cascades, funding extremes, positioning, and next risk zone. This conveys the tool's analytical behavior, though it does not discuss limits or non-determinism.

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 one dense sentence with no filler; the capability label is front-loaded and the parenthetical output details justify their length. Every clause adds information an agent can act on.

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 two-parameter tool with no annotations and no output schema, the description conveys purpose, input style, output substance, and data provenance in one sentence. The omission of the symbol parameter and any response-format details prevents a perfect score.

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 descriptions are absent (0% coverage), so the description must clarify the parameters. It meaningfully illustrates the 'question' parameter via a natural-language example, but it never mentions the optional 'symbol' parameter, its default, or what values it accepts.

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 a specific capability: it answers natural-language questions like 'Why did BTC drop 4%?' with a grounded market explanation. This clearly distinguishes it from the sibling get_* data-retrieval tools, since it synthesizes an answer rather than returning a metric series.

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 example 'Why did BTC drop 4%?' and 'AI risk-intelligence answer' establish when to use the tool: when an agent needs an interpreted explanation rather than raw data. It does not explicitly name sibling tools or state when not to use it, so it falls short of a 5.

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