FLAGSHIP: short-squeeze / long-flush score 0-100 for any USDT perp. Composite of funding, long/short crowding, 24h OI build, and liq-skew from our exclusive tape. One number that answers "is this trade crowded and about to hurt someone". Costs $0.1 USDC per call (x402, Solana mainnet).
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | Yes | USDT perp symbol e.g. SOLUSDT |
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, the description carries the behavioral disclosure burden. It transparently discloses the paid nature of the call ($0.1 USDC via x402 on Solana mainnet), the scoring range, and the composite factors. It does not mention auth, failure modes, or rate limits, but the cost and payment rail disclosure is substantial and beyond what the schema offers.
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 compact and front-loaded: it states what the tool provides, the score range, the components, the use case, and the cost in three sentences. Every sentence earns its place and there is no redundant filler.
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 single-parameter tool with no output schema, the description explains the output concept (a 0-100 score), the domain (USDT perps), the inputs it composites, and the cost. It does not specify the exact response structure, but 'one number' plus 'score 0-100' gives an agent enough to understand the return value.
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% and the schema already explains that symbol is a USDT perp symbol like SOLUSDT. The description adds context like 'any USDT perp' but does not materially expand the parameter semantics, so the baseline of 3 is appropriate.
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 a specific output (short-squeeze / long-flush score 0-100 for USDT perps) and explains what the score is composed of. It does not explicitly name or contrast itself with sibling tools like get_long_short or get_positioning, so it stops short of full sibling differentiation.
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 gives a concrete use case: answering whether a trade is crowded and about to hurt someone, and it scopes the tool to any USDT perp. It does not explicitly state when not to use it or point to alternatives, but the usage context is clear enough for an agent to select it purposefully.
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.