Most crowded trades across ~600 USDT perps: top most-positive and most-negative funding with annualized %, 24h price move and OI. Crowded shorts = squeeze candidates. Costs $0.02 USDC per call (x402, Solana mainnet).
| Name | Required | Description | Default |
|---|---|---|---|
| limit | No | top N each side, 1-25, default 10 | |
| min_turnover_usd | No | liquidity floor, default 1M |
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Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations, the description carries the behavioral disclosure burden. It adds meaningful behavioral details: the ~600 USDT perps universe, the annualized funding format, inclusion of 24h price move and OI, and the $0.02 USDC per-call cost on x402/Solana mainnet. This goes well beyond a bare statement of purpose, though it does not cover rate limits or authentication details.
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?
Three tight sentences: the first explains the product and output fields, the second gives the analytical interpretation, and the third states cost and network. There is no fluff and key information is front-loaded.
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 read-like data tool with simple optional parameters and no output schema, the description covers the important context: scope, returned metrics, practical interpretation, and cost. An agent has enough information to decide when to invoke it and what to expect.
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%, with limit and min_turnover_usd both described. The description does not add extra parameter semantics beyond the schema, so the baseline 3 applies.
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 states what the tool returns: the most positive and most negative funding extremes across ~600 USDT perps, including annualized %, 24h price move, and OI. This distinguishes it from sibling tools like get_funding_rate or get_funding_history, which are about individual funding rates or histories.
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 provides clear context: it identifies crowded trades and explicitly calls out 'Crowded shorts = squeeze candidates,' signaling when this tool is useful. It does not explicitly name alternative tools or state when not to use it, so it misses the full exclusion guidance.
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.