Crypto Liquidations MCP

Crypto Liquidations MCP bridges the gap between real‑time market data and AI assistants by streaming Binance liquidation events directly into a Claude (or any MCP‑compatible) workflow. Liquidations—forceful closing of leveraged positions—are a key indicator of market stress and can precede sharp price movements. By exposing these events through a lightweight, in‑memory feed, the server lets developers and traders capture volatility signals instantly without polling APIs or managing complex data pipelines.

The core value proposition lies in its real‑time streaming capability. The server connects to Binance’s WebSocket endpoint () and pushes each liquidation as it occurs. An internal buffer holds the most recent 1,000 events, allowing quick historical queries while keeping memory usage predictable. This design eliminates the need for persistent storage or batch processing, making it ideal for latency‑sensitive applications such as automated trading bots or market‑watch dashboards.

Key features include:

• tool: Retrieves the latest events in a Markdown table with columns for symbol, side (BUY/SELL), price, quantity, and timestamp. The parameter lets callers request up to 1,000 rows, defaulting to ten.
• prompt: Generates a structured analysis template that guides an AI assistant to evaluate liquidation trends across all symbols, leveraging the tool for data retrieval.
• In‑memory storage: No disk writes mean zero I/O latency and easy scaling; the buffer can be reset or cleared programmatically if needed.

Typical use cases span both retail and institutional contexts. A day trader can ask the assistant, “Show me the latest liquidations,” to spot sudden sell‑side pressure before a price dip. A portfolio manager might request an analysis of liquidation frequency to adjust risk limits or hedge positions proactively. Moreover, developers can embed the MCP into larger AI pipelines—such as anomaly detection or sentiment analysis—to enrich models with fresh market signals without re‑engineering the data ingestion layer.

Integration is straightforward: once the MCP server is running, any Claude or other MCP client can invoke as a tool call, receiving a ready‑to‑use Markdown table. The prompt can be passed to the assistant’s prompt engine, which will internally call the tool and produce a narrative analysis. This seamless coupling allows AI agents to react instantly to market events, automate decision logic, and provide actionable insights—all within the familiar conversational interface.