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Glama

derivatives.funding_rate

Gauge long/short crowding and spot funding-rate arbitrage with current perpetual futures funding rates from Bybit (Binance fallback) before entering or hedging positions.

Instructions

Get perpetual futures funding rate (Bybit primary, Binance fallback) to
gauge long/short crowding before entering or hedging a position.

Use this when asked about funding rate levels or funding-rate arbitrage/carry
trade opportunities. If you also need the trade annualized into an APR with
a carry-trade breakeven-days estimate, use get_funding_apr_matrix instead -
it already calls this internally, so calling both is redundant. Check the
data_source field to see whether Bybit or the Binance fallback answered;
predicted_rate equals funding_rate because neither exchange exposes a
separate forecast field (not a bug). Also returns mark_price/index_price
(populated on both the Bybit and Binance paths) and open_interest_usd
(Bybit path only - always null on the Binance fallback, since Binance's
premiumIndex endpoint doesn't report open interest).

Args:
    symbol: e.g. "BTC", "ETH", or "BTCUSDT" (non-USDT symbols are
        normalized to USDT pairs).

Returns:
    On success: {"success": true, "funding_rate_percentage",
        "funding_interval_hours", "mark_price", "index_price",
        "open_interest_usd", "data_source", ...}
    On failure: {"success": false, "error": {"type", "message"}}

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
symbolYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. Addedv1.0.3

TDQS

A5/5.0
Behavior5/5

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

With no annotations, the description carries the full behavioral burden and does so thoroughly: it discloses the Bybit-first/Binance-fallback data_source behavior, explains that predicted_rate equals funding_rate is not a bug, and documents when open_interest_usd is always null. These are exactly the behavioral quirks an agent needs before trusting the result.

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 purpose and usage guidance are front-loaded, and each subsequent sentence adds a distinct piece of behavior or return-field context. The Args and Returns sections organize technical detail cleanly, with no filler.

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

Completeness5/5

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

With no output schema, the description enumerates the success fields, the failure structure, per-data-source field availability, and the key fallback caveat. For a one-parameter read tool with complex sourcing behavior, this is a complete invocation contract.

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

Parameters5/5

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

Schema coverage is 0%, but the description fully compensates for the sole symbol parameter by giving concrete examples ('BTC', 'ETH', 'BTCUSDT') and the normalization rule for non-USDT symbols. This leaves no ambiguity about what to pass.

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 opens with a specific verb and resource: 'Get perpetual futures funding rate', names the primary and fallback sources, and gives an intended use case. It also explicitly differentiates this tool from get_funding_apr_matrix, so an agent can distinguish them immediately.

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

Usage Guidelines5/5

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

The description states exactly when to use this tool ('when asked about funding rate levels or funding-rate arbitrage/carry trade opportunities') and names the alternative with an explicit exclusion: use get_funding_apr_matrix instead when APR/breakeven is needed, since calling both is redundant.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.