Fear & Greed Index▌
by ycjcl868
Access CNN's Fear & Greed Index for US stock market sentiment, including Dow Jones Industrial Average index momentum and
Provides real-time access to CNN's Fear & Greed Index for US stock market sentiment analysis, retrieving current composite scores and seven individual market indicators including S&P 500 momentum, options ratios, and volatility measures with historical comparisons.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / Stock traders analyzing market sentiment
- / Financial analysts tracking market fear levels
- / Investment researchers studying market psychology
- / Portfolio managers timing market entries
capabilities
- / Fetch current Fear & Greed Index score (0-100)
- / Get historical sentiment comparisons
- / Access seven individual market indicators
- / Retrieve S&P 500 momentum data
- / Monitor options put/call ratios
- / Track VIX volatility measures
what it does
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.
how to install
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.
license
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.
readme
MCP Server Fear & Greed Index
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
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"
]
}
}
}
<details><summary><b>Install in VS Code</b></summary>
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"
]
}
}
}
</details>
<details>
<summary><b>Install in Windsurf</b></summary>
Follow Windsurf MCP documentation. Use following configuration:
{
"mcpServers": {
"mcp-server-fear-greed": {
"command": "npx",
"args": [
"mcp-server-fear-greed@latest"
]
}
}
}
</details>
<details>
<summary><b>Install in Claude Desktop</b></summary>
Follow the MCP install guide, use following configuration:
{
"mcpServers": {
"mcp-server-fear-greed": {
"command": "npx",
"args": [
"mcp-server-fear-greed@latest"
]
}
}
}
</details>
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
FAQ
- What is the Fear & Greed Index MCP server?
- Fear & Greed Index is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Fear & Greed Index?
- This profile displays 35 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.7 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ 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.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.7★★★★★35 reviews- ★★★★★Dhruvi Jain· Dec 28, 2024
Fear & Greed Index reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Valentina Agarwal· Dec 16, 2024
Fear & Greed Index is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Oshnikdeep· Nov 19, 2024
I recommend Fear & Greed Index for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Anika Diallo· Nov 19, 2024
According to our notes, Fear & Greed Index benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Meera Martinez· Nov 7, 2024
Fear & Greed Index is among the better-indexed MCP projects we tried; the explainx.ai summary tracks the official description.
- ★★★★★Meera Zhang· Oct 26, 2024
We evaluated Fear & Greed Index against two servers with overlapping tools; this profile had the clearer scope statement.
- ★★★★★Ganesh Mohane· Oct 10, 2024
Strong directory entry: Fear & Greed Index surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Soo Flores· Oct 10, 2024
Fear & Greed Index has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Mia Jain· Sep 21, 2024
Fear & Greed Index reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Meera Diallo· Sep 17, 2024
Useful MCP listing: Fear & Greed Index is the kind of server we cite when onboarding engineers to host + tool permissions.
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