Headline Vibes Analysis MCP Server
The Headline Vibes Analysis MCP Server analyzes sentiment in news headlines from major US publications.
Analyze Sentiment: Evaluates up to 100 headlines per request on a scale of 0 (most negative) to 10 (most positive).
Date Flexibility: Supports both natural language date input (e.g., "yesterday", "last Friday") and specific date formats (e.g., "2025-02-11").
Source Diversity: Pulls headlines from major US news sources like Reuters, CNN, Associated Press, and Wall Street Journal.
Detailed Results: Provides sentiment score, synopsis, number of headlines analyzed, source distribution, and up to 5 sample headlines.
Error Handling: Includes clear error messages for issues like invalid dates or API errors.
Development Support: Offers watch mode for real-time server testing during development.
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@Headline Vibes Analysis MCP Serveranalyze headlines from yesterday"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
Headline Vibes — EventRegistry MCP Server
Headline Vibes is a Model Context Protocol server that analyzes investor sentiment in US news headlines fetched from EventRegistry (newsapi.ai). It supports daily and monthly analysis modes, produces structured outputs with diagnostics, and runs over stdio or HTTP (Railway-ready).
Features
Curated US newsroom coverage with investor relevance filtering
Dual sentiment scores (general + investor) normalized to a 0–10 scale
Political-leaning breakdowns, source distributions, and sampling diagnostics
Natural-language date parsing for daily requests (
"yesterday","last Friday", etc.)Structured JSON outputs compatible with MCP
structuredContentBuilt-in token budgeting and rate-limit telemetry
Related MCP server: News Analysis Agent MCP Server
Prerequisites
Node.js v18+ (LTS recommended)
EventRegistry API key (https://newsapi.ai)
Setup
Install dependencies and build:
npm install npm run buildConfigure environment variables (stdio example):
{ "mcpServers": { "headline-vibes": { "command": "node", "args": ["/absolute/path/headline-vibes/build/index.mjs"], "env": { "NEWS_API_KEY": "your-eventregistry-key", "TRANSPORT": "stdio" } } } }For HTTP (Railway) deployments, set
TRANSPORT=http,HOST=0.0.0.0,PORT=<port>, and optionallyALLOWED_HOSTS/ALLOWED_ORIGINS.
Available Tools
analyze_headlines
Daily sentiment snapshot for a single day. Arguments: { "input": string }.
Accepts natural language or
YYYY-MM-DD.Returns investor/general scores, synopses, distributions, sample headlines, and diagnostics.
analyze_monthly_headlines
Monthly aggregation between two months. Arguments: { "startMonth": "YYYY-MM", "endMonth": "YYYY-MM" }.
Outputs per-month political sentiments, headline counts, and token/sampling diagnostics.
JSON schemas powering structured results live in src/schemas/headlines.ts.
Development & Testing
Watch mode:
npm run watchStdio run:
NEWS_API_KEY=... npm run start:stdioHTTP run:
TRANSPORT=http HOST=0.0.0.0 PORT=8787 NEWS_API_KEY=... npm run start:httpSmoke check EventRegistry connectivity:
node ./build/scripts/smoke.mjs 2025-02-01Unit tests (Vitest):
npm test
Railway Deployment
Set environment variables (
TRANSPORT,HOST,PORT,NEWS_API_KEY, optionalALLOWED_HOSTS,ALLOWED_ORIGINS,LOG_LEVEL).Build once locally (
npm run build) or via Railway’s build step.Start with
npm run start.Health probe:
GET /healthzreturns200 ok.
See docs/railway.md for the full playbook and docs/claude-desktop.md for Claude Desktop integration steps.
License
MIT
This server cannot be deployed
Maintenance
Related MCP Connectors
News sentiment score trends for any topic over time. Free key at trendsapi.ai
News sentiment scores over time, with growth for any topic. Free key at trendsmcp.ai
Get access to real-time and historical news data including top headlines from global sources
Real-time news search across 500,000+ sources in 60+ languages with sentiment and entities.
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