Perpetual futures funding rates across Hyperliquid and Gate with crowded-trade extremes and squeeze-risk synthesis. $0.05 USDC. Payment is consumed on execution, including timeouts.
| Name | Required | Description | Default |
|---|---|---|---|
| symbol | No | Asset symbol (e.g., BTC). Omit for market-wide extremes | |
| exchange | No | Limit to one exchange: gate or hyperliquid. Default: both |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
No annotations are provided, so the description carries full burden. It discloses cost ($0.05 USDC) and that payment is consumed on execution including timeouts, which is helpful. However, it does not mention read-only nature, rate limits, or response format, leaving gaps in behavioral transparency.
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 sentences with no wasted words. The first sentence captures the core functionality, and the second sentence adds essential cost behavior. Information is front-loaded effectively.
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 complexity of funding rates across two exchanges with squeeze-risk synthesis and no output schema, the description lacks details about return format, pagination, or how results are structured. It covers cost and core purpose but leaves some completeness gaps.
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 description adds value by stating the supported exchanges (Hyperliquid, Gate) and that omitting symbol gives market-wide extremes. This provides practical context 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?
The description clearly states the tool provides perpetual futures funding rates across Hyperliquid and Gate, with crowded-trade extremes and squeeze-risk synthesis. It also specifies the cost ($0.05 USDC) and payment consumption. The verb 'funding rates' and resource specification distinguish it from sibling alpha_* 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 description implies usage when funding rates or squeeze-risk synthesis is needed, but it does not provide explicit guidance on when to use this tool over alternatives, such as alpha_deep or alpha_risk. No exclusions or comparative context are given.
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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Each tool targets a distinct aspect of crypto alpha research (e.g., brief, calendar, compare, deep, macro, memecoin, narrative, news, onchain, perps_funding, portfolio, prediction, risk, search, sentiment, stats, subscribe, token, trending). Descriptions clearly differentiate purposes, minimizing ambiguity.
All tool names follow a uniform 'alpha_{descriptive_noun}' pattern with snake_case, making naming predictable and easy to navigate.
With 19 tools spanning a broad range of crypto intelligence (market data, sentiment, on-chain, risk, portfolio, news, etc.), the count is well-scoped for the server's purpose—neither too few nor excessive.
The tool set covers most key areas of crypto research (price, sentiment, on-chain, risk, news, calendar, narratives, portfolio, predictions, subscriptions). Minor gaps like a dedicated volume/anomaly tool are absent, but the set is largely comprehensive.