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Fear & Greed Index MCP Server

by ycjcl868
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TypeScript
MIT License
16
4

MCP Server Fear & Greed Index

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A Model Context Protocol (MCP) server that provides access to the CNN Fear & Greed Index for the US stock market. This server fetches real-time market sentiment data and presents it in both structuredContent and text content.

Features

  • Real-time Fear & Greed Index: Get the current market sentiment score (0-100)

  • Historical Comparisons: View previous close, week, month, and year data

  • Detailed Market Indicators: Access individual component scores including:

    • Market Momentum (S&P 500 & S&P 125)

    • Stock Price Strength & Breadth

    • Put/Call Options Ratio

    • Market Volatility (VIX)

    • Junk Bond Demand

    • Safe Haven Demand

  • Flexible Output: Choose between structured markdown or raw JSON format

Related MCP server: Global MCP Stock Server

Requirements

  • Node.js 18 or newer

  • VS Code, Cursor, Windsurf, Claude Desktop or any other MCP client

Getting Started

Local (Stdio)

First, install the Fear & Greed MCP server with your client. A typical configuration looks like this:

{ "mcpServers": { "mcp-server-fear-greed": { "command": "npx", "args": [ "-y", "mcp-server-fear-greed@latest" ] } } }

You can also install the mcp-server-fear-greed MCP server using the VS Code CLI:

# For VS Code code --add-mcp '{"name":"mcp-server-fear-greed","command":"npx","args":["mcp-server-fear-greed@latest"]}'

After installation, the Fear & Greed MCP server will be available for use with your GitHub Copilot agent in VS Code.

Go to Cursor Settings -> MCP -> Add new MCP Server. Name to your liking, npx mcp-server-fear-greed. You can also verify config or add command like arguments via clicking Edit.

{ "mcpServers": { "mcp-server-fear-greed": { "command": "npx", "args": [ "mcp-server-fear-greed@latest" ] } } }

Follow Windsurf MCP documentation. Use following configuration:

{ "mcpServers": { "mcp-server-fear-greed": { "command": "npx", "args": [ "mcp-server-fear-greed@latest" ] } } }

Follow the MCP install guide, use following configuration:

{ "mcpServers": { "mcp-server-fear-greed": { "command": "npx", "args": [ "mcp-server-fear-greed@latest" ] } } }

Remote (SSE / Streamable HTTP)

At the same time, use --port $your_port arg to start the browser mcp can be converted into SSE and Streamable HTTP Server.

# normal run remote mcp server npx mcp-server-fear-greed --port 8089

You can use one of the two MCP Server remote endpoint:

  • Streamable HTTP(Recommended): http://127.0.0.1::8089/mcp

  • SSE: http://127.0.0.1::8089/sse

And then in MCP client config, set the url to the SSE endpoint:

{ "mcpServers": { "mcp-server-fear-greed": { "url": "http://127.0.0.1::8089/sse" } } }

url to the Streamable HTTP:

{ "mcpServers": { "mcp-server-fear-greed": { "type": "streamable-http", // If there is MCP Client support "url": "http://127.0.0.1::8089/mcp" } } }

In-memory call

If your MCP Client is developed based on JavaScript / TypeScript, you can directly use in-process calls to avoid requiring your users to install the command-line interface to use Fear & Greed MCP.

import { Client } from '@modelcontextprotocol/sdk/client/index.js'; import { InMemoryTransport } from '@modelcontextprotocol/sdk/inMemory.js'; // type: module project usage import { createServer } from 'mcp-server-fear-greed'; // commonjs project usage // const { createServer } = await import('mcp-server-fear-greed') const client = new Client( { name: 'test fear greed client', version: '1.0', }, { capabilities: {}, }, ); const server = createServer(); const [clientTransport, serverTransport] = InMemoryTransport.createLinkedPair(); await Promise.all([ client.connect(clientTransport), server.connect(serverTransport), ]); // list tools const result = await client.listTools(); console.log(result); // call tool const toolResult = await client.callTool({ name: 'get_fear_greed_index', arguments: { format: 'json' }, }); console.log(toolResult);

API Reference

Tool: get_fear_greed_index

Fetches the current Fear & Greed Index and related market indicators.

Parameters

  • format (optional): Output format

    • "structured" (default): Returns formatted markdown with organized data

    • "json": Returns raw JSON data

Example Usage

// Get structured output await client.callTool("get_fear_greed_index"); // Get JSON output await client.callTool("get_fear_greed_index", { format: "json" });

Response Structure

The tool returns data in the following structure:

{ "fear_and_greed": { "score": 75, "rating": "greed", "timestamp": "2025-07-18T23:59:57+00:00", "previous_close": 75.31, "previous_1_week": 75.26, "previous_1_month": 54.29, "previous_1_year": 45.94 }, "fear_and_greed_historical": { "timestamp": 1752883197000, "score": 75, "rating": "greed" }, "market_momentum_sp500": { "timestamp": 1752871567000, "score": 61.2, "rating": "greed" }, "market_momentum_sp125": { "timestamp": 1752871567000, "score": 61.2, "rating": "greed" }, "stock_price_strength": { "timestamp": 1752883197000, "score": 80, "rating": "extreme greed" }, "stock_price_breadth": { "timestamp": 1752883197000, "score": 84, "rating": "extreme greed" }, "put_call_options": { "timestamp": 1752871897000, "score": 79.6, "rating": "extreme greed" }, "market_volatility_vix": { "timestamp": 1752869701000, "score": 50, "rating": "neutral" }, "market_volatility_vix_50": { "timestamp": 1752869701000, "score": 50, "rating": "neutral" }, "junk_bond_demand": { "timestamp": 1752877800000, "score": 88.8, "rating": "extreme greed" }, "safe_haven_demand": { "timestamp": 1752868799000, "score": 81.4, "rating": "extreme greed" } }

Fear & Greed Index Ratings

The index uses the following rating scale:

  • 0-25: Extreme Fear

  • 26-45: Fear

  • 46-55: Neutral

  • 56-75: Greed

  • 76-100: Extreme Greed

Development

Access http://127.0.0.1:6274/:

npm run dev

Error Handling

The server includes comprehensive error handling:

  • Network request failures are caught and reported

  • Invalid API responses are handled gracefully

  • Missing data fields are filled with sensible defaults

  • All errors include descriptive messages

Deploy Server
A
security – no known vulnerabilities
A
license - permissive license
A
quality - confirmed to work

How are these scores calculated?

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