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VENTURE-AI-LABS

CryptoDataAPI MCP Server

get_funding_rates

Retrieve perpetual funding rates across cryptocurrency exchanges to analyze market sentiment and identify potential corrections or capitulation signals.

Instructions

Get cross-exchange perpetual funding rates. Positive funding means longs pay shorts (bullish crowding); negative funding means shorts pay longs (bearish crowding). Extreme positive rates (>0.05%) often precede corrections; extreme negative rates often signal capitulation. Returns data from multiple exchanges including Binance, Bybit, OKX, and more.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
exchangeNoFilter by exchange name (e.g., 'binance', 'hyperliquid'). Omit for all exchanges.
typeNoMarket type filter (e.g., 'perps'). Omit for default.
limitNoNumber of results to return. Default 250.

Implementation Reference

  • The handler function that executes the logic to fetch funding rates from the API.
    export async function handler(args: z.infer<typeof schema>) {
      return apiGet("/api/v1/market-intelligence/funding-rates", {
        exchange: args.exchange,
        type: args.type,
        limit: args.limit,
      });
    }
  • The input schema for the get_funding_rates tool using zod.
    export const schema = z.object({
      exchange: z
        .string()
        .optional()
        .describe("Filter by exchange name (e.g., 'binance', 'hyperliquid'). Omit for all exchanges."),
      type: z
        .string()
        .optional()
        .describe("Market type filter (e.g., 'perps'). Omit for default."),
      limit: z
        .number()
        .optional()
        .describe("Number of results to return. Default 250."),
    });
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations provided, the description carries full burden and adds valuable behavioral context: it explains what funding rates mean (longs/shorts payments), market implications (bullish/bearish crowding, corrections, capitulation), and lists example exchanges covered. However, it lacks details on rate limits, authentication needs, or data freshness.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is appropriately sized and front-loaded: the first sentence states the core purpose, followed by explanatory context and coverage details. Every sentence adds value without redundancy.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given no annotations and no output schema, the description provides good context on behavior and coverage, but could be more complete by specifying return format (e.g., structured data with timestamps) or error handling for a tool with 3 parameters.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3. The description does not add parameter-specific semantics beyond the schema, such as explaining default behaviors for omitted parameters or providing additional examples for exchange names.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool's purpose with specific verb ('Get') and resource ('cross-exchange perpetual funding rates'), and distinguishes it from siblings by focusing on funding rates rather than other market data like liquidations, open interest, or ETF flows.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description implies usage by explaining the significance of positive/negative funding rates and extreme values, but does not explicitly state when to use this tool versus alternatives like get_open_interest or get_liquidations for related market insights.

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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