Largest resting orders each side of the book for any USDT perp, with USD size and distance from mid. Costs $0.01 USDC per call (x402, Solana mainnet).
| Name | Required | Description | Default |
|---|---|---|---|
| top | No | walls per side, 1-15, default 5 | |
| symbol | Yes | USDT perp symbol e.g. SOLUSDT |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries full burden. It transparently discloses that the call costs $0.01 USDC via x402 on Solana mainnet, which is important behavioral and economic context. It also describes what data the tool returns, including largest resting orders, USD size, and distance from mid. It does not discuss edge cases such as empty books, but the key operational trait (paid call) is disclosed.
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 sentences with no redundancy: the first states the purpose and output, the second gives the cost and payment rail. The key differentiator, cost, is placed early in the second sentence. Every word earns its place.
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 simple two-parameter tool with no output schema, the description is largely complete: it explains the output content, scope, and a critical paid-call caveat. Missing guidance about choosing it over sibling orderbook tools is the main gap, but the low complexity keeps the absence from being fatal.
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 description coverage is 100%, so both `symbol` and `top` are already documented in the schema. The description reinforces the symbol type by saying 'any USDT perp' but adds no new parameter-specific meaning beyond that. This earns the baseline 3.
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 uses a specific verb and resource: it identifies orderbook walls as the largest resting orders on each side of the book for USDT perps, and specifies the output includes USD size and distance from mid. It is clear about scope, though it does not explicitly differentiate itself from the closely related sibling get_orderbook_imbalance.
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?
No guidance is given on when to use this tool versus alternatives like get_orderbook_imbalance or other orderbook-related tools. The only usage hint is the generic 'for any USDT perp' scope, which describes applicability but not selection criteria.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
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Many tools are tightly scoped and cross-referenced, but the set contains overlapping families: liquidation tools (alert/scan/history/stats/leaders/recent/heatmap) and redundant snapshots like get_market_snapshot vs get_trade_context, get_last_liquidation vs get_recent_liquidations, and get_cascade_forecast_free vs get_cascade_forecast. Agents will need to read descriptions carefully to avoid misselection.
All tool names follow a consistent get_<domain>_<detail> snake_case pattern, which makes the API predictable. The only real deviations are the bare 'pricing' tool and the 'free' suffix on taster variants.
At 52 tools, this far exceeds the 3-15 well-scoped range and crosses the 50-tool extreme threshold. The count is inflated by numerous paid/free taster pairs and many overlapping liquidation variants.
The surface covers prices, funding, open interest, orderbooks, liquidations, wallet/token data, Solana network health, DeFi TVL, and stablecoin flows—broad coverage for a crypto data feed. Gaps like historical OHLC/price candles, a machine-readable symbol list, and pagination endpoints are workable around but would round it out.