Funding Rates MCP

Funding Rates MCP

The Funding Rates MCP fills a critical niche for crypto traders and AI agents that need up‑to‑date market data. Funding rates are periodic payments exchanged between long and short positions on perpetual contracts; they reveal market sentiment, liquidity pressure, and potential arbitrage windows. By exposing these rates through a standard Model Context Protocol interface, the server lets AI assistants fetch and compare funding across multiple exchanges in real time, empowering agents to surface profitable opportunities or flag anomalous pricing.

At its core the server pulls current funding data from six major crypto exchanges—Binance, OKX, Bybit, Bitget, Gate, and CoinEx. It then formats the information into a pivoted Markdown table: symbols form the rows, exchanges become columns, and an additional Divergence column shows the maximum funding rate difference for each symbol. This layout is immediately consumable by an AI assistant, which can present the table to a user or trigger further actions such as placing trades. The design keeps the output human‑readable while still being machine‑parseable, a key advantage for agents that need to interpret or transform the data.

Key capabilities include:

• Real‑time freshness – The server queries each exchange’s API on demand, ensuring that agents always see the latest rates without caching delays.
• Configurable scope – Users can request specific symbols or limit the comparison to a subset of exchanges, allowing tailored queries for niche pairs or region‑specific markets.
• Integrated prompts – A dedicated prompt generator creates natural language queries for Claude Desktop, making it easy to translate a user’s spoken or typed request into the underlying tool call.
• Markdown‑ready output – The pivoted table is returned as Markdown, which Claude can render directly in chat or embed in reports.

Typical use cases span both individual and institutional workflows. A retail trader might ask an AI assistant to “compare funding rates for BTC/USDT and ETH/USDT across all exchanges” and receive an instant table highlighting a 0.02 % divergence that signals a potential arbitrage window. A quantitative research team could embed the MCP tool in an automated strategy pipeline, continuously scanning for symbols whose funding divergence exceeds a threshold and triggering back‑testing or live orders. Even compliance teams can use the data to monitor for abnormal funding spikes that could indicate market manipulation.

Integration into AI workflows is straightforward: the MCP server exposes a single tool that accepts a list of symbols and optional exchange filters. Claude or any other MCP‑compatible assistant can invoke this tool, receive the Markdown table, and then decide whether to present it, store it, or trigger downstream actions. Because the server is written in Python and supports modern dependency managers like uv, it can be deployed behind a reverse proxy or as a serverless function, fitting neatly into existing cloud architectures.

In summary, the Funding Rates MCP turns raw exchange data into a ready‑to‑use, AI‑friendly resource. By delivering real‑time funding rates in a concise, tabular format and coupling that with natural language prompt generation, it gives developers and traders an efficient gateway to identify arbitrage opportunities, monitor market health, and automate data‑driven decision making.