FREE taster: the full-quality liquidation forecast for SOL, no delay and nothing withheld. Use it to check the calibration before paying for coverage of the other ~345 symbols. Free.
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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 does add useful behavioral context: the tool is free, full-quality, has no delay, and withholds nothing. However, it doesn't describe what the returned forecast looks like, how the output should be interpreted, or how 'calibration' is presented, leaving some behavioral ambiguity.
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?
The description is short, front-loaded with the free-taster framing, and quickly establishes what the tool does and when to use it. It loses a small amount of efficiency because 'Free' appears twice, once in the opening label and once as a standalone final sentence, making the ending redundant.
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?
Given the zero-parameter schema and lack of output schema, the description is reasonably complete for selection purposes. However, it never explains what the forecast output contains, its format, or how an agent should consume it after invoking the tool. This is a notable gap because no output schema exists to fill that void.
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 schema coverage is 100%, so there is no parameter burden to carry. The description effectively communicates the key implicit parameter—the symbol SOL—by naming it explicitly. This matches the baseline for a zero-parameter tool.
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 the tool's core purpose: a liquidation forecast for SOL. It also distinguishes itself from siblings by being a free taster, covering only SOL while the paid option covers ~345 symbols. The phrase 'full-quality... no delay and nothing withheld' removes ambiguity about it being a degraded demo.
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 explicitly tells the agent when to use this tool: 'Use it to check the calibration before paying for coverage of the other ~345 symbols.' This implies the alternative is the paid, broader forecast tool, though it doesn't name get_cascade_forecast directly. It provides a clear use case but doesn't fully spell out when not to use it.
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.