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

With no annotations, the description carries the full burden and does disclose the important pay-per-call behavior: 0.002 USDC per call via x402. However, it omits data source/freshness, rate limits, and return behavior, leaving meaningful behavioral gaps for a paid data tool.

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?

Two tight sentences with no waste. The core description is front-loaded, followed by the cost/payment model, which is the right priority order.

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 universe, timeframe, filter, and payment model, which is adequate for a simple list tool. But with no output schema, it should ideally indicate return structure, sorting, and whether limit applies per side; those gaps leave it at minimum viable completeness.

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 67%, with paymentProof already well described in the schema. The description reinforces the minVolumeUsd filter but does not clarify the limit parameter's semantics, such as whether it applies to each side or total results.

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 names a specific resource: top gainers and losers among liquid USDT spot pairs over 24h, filtered by minimum quote volume. It is clearly narrower than generic crypto price or token market tools, though it does not explicitly name a sibling alternative.

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?

It implies a market-movers use case but gives no guidance on when to choose this over siblings like trending_tokens, new_token_profiles, ohlcv, or token_market. No exclusions or decision criteria are provided.

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