Liquidation aggregates for the 5 majors (SOL, BTC, ETH, XRP, DOGE): 1h and 24h totals, longs vs shorts USD split, biggest print, broken out per exchange. Costs $0.004 USDC per call (x402, Solana mainnet).
| Name | Required | Description | Default |
|---|---|---|---|
No arguments | |||
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the full burden. It transparently discloses the pay-per-call cost, the payment rail (x402), and the network (Solana mainnet), which are critical behavioral traits for an agent deciding whether to invoke it. It does not mention return format or error behavior, but for a read-only stats endpoint the cost and scope are the most important disclosures.
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 tightly packed sentences with no filler. The first sentence front-loads the resource and all relevant data components, and the second adds the cost detail. Every sentence contributes useful information.
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 zero-parameter tool with no output schema, the description is quite complete: it states the instruments, time windows, metric types, and cost. It does not specify the exact JSON return shape, but that is not essential for an agent to invoke the tool correctly.
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 and the input schema is an empty object with 100% coverage, so there are no parameter semantics to explain. The baseline of 4 applies because the description correctly focuses on the output content and cost rather than 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 clearly identifies the resource: liquidation aggregates for a specific set of 5 majors, with concrete data elements (1h/24h totals, longs vs shorts USD split, biggest print, per-exchange breakdown). This distinguishes it from sibling liquidation tools by scope, though it lacks an explicit verb like 'retrieve' and does not name a sibling.
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 implies the use case by specifying exactly what data is returned, so an agent can infer when this tool fits. However, it provides no explicit guidance on when to use this tool versus the many liquidation-related siblings such as get_recent_liquidations or get_liq_history, and no exclusion 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.