volume_what_if

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

Annotations declare readOnlyHint=true and openWorldHint=false, but the description goes well beyond them: it enumerates the four return blocks, discloses that holding-gated rungs (e.g. Binance spot BNB requirement) are flagged as unreachable by volume alone, and notes referral discounts and the platform-token toggle are applied. It also discloses sampling behavior and a warning at >16 points.

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?

Front-loaded with when-to-use, then the numbered output enumeration keeps the returns easy to scan. However, the bilingual Chinese/English pair largely restates the same content, which adds length without adding information for an agent.

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 13-parameter tool with no output schema, the description fully compensates by describing the return shape in four numbered blocks and the default sweep construction. An agent has enough to call it correctly and interpret the results without an output schema.

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%, so the schema already carries per-parameter meaning; baseline would be 3. The description adds semantic ties the schema omits, notably that referral/token discounts are applied 'exactly like compare_exchange_fees' and that baseVolume guarantees the point appears in the sweep, giving cross-tool consistency context.

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?

States a specific operation (scan monthly volume from zero through the VIP ladder) and a specific output (fee-cost sensitivity curve) for spot OR futures across every allowed venue. It distinguishes itself from siblings by explicitly naming compare_total_cost for the full cost stack and compare_exchange_fees as the discount-model analog.

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?

Opens with concrete user question triggers (fee at volume X, growth scenarios, VIP upgrade, tier jump points, cross-volume curves) in both Chinese and English, then specifies when to pass explicit volumes vs rely on the default union-of-thresholds sampling. It also states the scope boundary and the alternative tool to use outside it.

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