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kukapay/crypto-feargreed-mcp

by Various

Providing real-time and historical Crypto Fear & Greed Index data

K

MCP

kukapay/crypto-feargreed-mcp

Added 1 June 2026

Overview

This MCP server provides real-time and historical Crypto Fear & Greed Index data via the Model Context Protocol. It exposes tools that allow AI agents to query the current index value and historical data for sentiment analysis.

Best for

Best for
Developers building AI agents for crypto trading or sentiment analysis who need quick MCP access to the Fear & Greed Index.

Use cases

  • Integrate Fear & Greed index into crypto trading agents
  • Fetch historical sentiment data for strategy backtesting
  • Monitor real-time market sentiment in AI dashboards

How to use

Install

npx -y @smithery/cli install @kukapay/crypto-feargreed-mcp --client claude

Tested with

Claude Desktop

Example client config

{ \n  "crypto-feargreed-mcp": { \n    "command": "uv", \n    "args": [ \n      "--directory", "/your/path/to/crypto-feargreed-mcp", \n      "run", \n      "main.py" \n    ]\n  } \n}

Notes

This MCP server provides real-time and historical Crypto Fear & Greed Index data via the Model Context Protocol. It exposes tools that allow AI agents to query the current index value and historical data for sentiment analysis.

53 stars on GitHub. Last updated 2025-05-10. Licensed MIT.

Use cases

  • Integrate Fear & Greed index into crypto trading agents
  • Fetch historical sentiment data for strategy backtesting
  • Monitor real-time market sentiment in AI dashboards

Pros

  • Delivers both real-time and historical index values
  • Simple integration for any MCP-compatible runtime
  • Lightweight Python implementation with clear API

Cons

  • Requires an MCP-compatible AI agent framework to use
  • Data accuracy depends entirely on the upstream Alternative.me source
  • Only covers a single sentiment indicator, not comprehensive market data

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Delivers both real-time and historical index values
  • Simple integration for any MCP-compatible runtime
  • Lightweight Python implementation with clear API

Cons

  • Requires an MCP-compatible AI agent framework to use
  • Data accuracy depends entirely on the upstream Alternative.me source
  • Only covers a single sentiment indicator, not comprehensive market data
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