24h top gainers and losers across ~600 USDT perps with a liquidity floor, funding attached. The "what moved" screener. Costs $0.01 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 |
Changes observed during successful MCP inspections. Dates show when Glama detected each change.
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
With no annotations provided, the description carries the disclosure burden and does disclose meaningful non-schema traits: a $0.01 USDC cost per call via x402 on Solana mainnet, funding data being attached, and the ~600-perp universe. It does not explicitly state that the call is read-only, but the tool name and screener framing make that a minor omission.
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 two tightly written sentences with no redundant phrasing. Core scope is front-loaded, and the cost/network detail is appended cleanly as supplementary operational 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?
Given there is no output schema and no annotations, the description covers the essential context: universe, time window, data included, cost, and network. It does not describe exact return shape or ordering, but for a simple two-parameter screener the definition is largely complete.
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 the schema already fully documents limit and min_turnover_usd, including defaults and bounds. The description adds only contextual gloss such as "liquidity floor" and "funding attached," which is helpful but not necessary for parameter understanding.
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 that the tool screens 24h top gainers and losers across ~600 USDT perps, naming both a specific resource and the screening action. It also calls itself the "what moved" screener, which distinguishes it from the many other market-data sibling tools.
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 phrase "what moved" screener implies this tool is for reviewing significant 24h perp price moves, but it does not explicitly say when to choose it over alternatives. It names no sibling tools, exclusion criteria, or complementary tools, leaving routing partially to inference.
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.