MCP server
by ycjcl868
Access CNN's Fear & Greed Index for US stock market sentiment, including Dow Jones Industrial Average index momentum and
Retrieves real-time CNN Fear & Greed Index data to analyze US stock market sentiment. Provides both the main composite score and seven individual market indicators like VIX volatility and put/call ratios.
You can install Fear & Greed Index in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server runs locally on your machine via the stdio transport. This server supports remote connections over HTTP, so no local installation is required.
MIT
Fear & Greed Index is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
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Fear & Greed Index reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Fear & Greed Index is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
I recommend Fear & Greed Index for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
According to our notes, Fear & Greed Index benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
Fear & Greed Index is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
We evaluated Fear & Greed Index against two servers with overlapping tools; this profile had the clearer scope statement.
Strong directory entry: Fear & Greed Index surfaces stars and publisher context so we could sanity-check maintenance before adopting.
Fear & Greed Index has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
Fear & Greed Index reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
Useful MCP listing: Fear & Greed Index is the kind of server we cite when onboarding engineers to host + tool permissions.
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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.
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.
</details> <details> <summary><b>Install in Cursor</b></summary>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"
]
}
}
}
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:
http://127.0.0.1::8089/mcphttp://127.0.0.1::8089/sseAnd 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"
}
}
}
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);
get_fear_greed_indexFetches the current Fear & Greed Index and related market indicators.
format (optional): Output format
"structured" (default): Returns formatted markdown with organized data"json": Returns raw JSON data// Get structured output
await client.callTool("get_fear_greed_index");
// Get JSON output
await client.callTool("get_fear_greed_index", { format: "json" });
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"
}
}
The index uses the following rating scale:
Access http://127.0.0.1:6274/:
npm run dev
The server includes comprehensive error handling:
Prerequisites
Time Estimate
15-60 minutes depending on server complexity
Steps
Troubleshooting
✓ Do
✗ Don't
💡 Pro Tips
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
Compatibility
✓ Use when
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid when
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.