Realized volatility for any USDT perp: 7d and 30d annualized from daily closes, plus today's range. Position-sizing input. Costs $0.01 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?
No annotations are provided, so the description carries the burden of behavioral disclosure. It reveals the paid nature of the call ($0.01 USDC), the x402/Solana mainnet payment rail, and the calculation basis (daily closes, annualized) — meaningful context beyond the schema. It omits details like output format or error cases, but this is acceptable for a simple data tool.
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 short sentences deliver the output definition, the use case, and the cost without wasted words. The most important 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 one-parameter tool with no output schema, the description covers the return values (7d/30d volatility and today's range), the pricing, and the use case. Nothing essential is missing for an agent to call it 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 schema already fully documents the single symbol parameter with an example, so description-level parameter detail is not critical. The description's 'any USDT perp' phrasing adds only slight scope clarification beyond the schema.
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?
Description states exactly what the tool computes: realized volatility for USDT perpetuals, with 7d and 30d annualized values and today's range. This distinguishes it from the many sibling get_* market tools, and the name reinforces the resource.
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?
Explicitly labels the tool as a position-sizing input, which tells an agent when this metric is relevant. It does not name sibling alternatives or state when not to use it, but the use-case guidance is clear for a one-purpose metrics tool.
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.