# Stanford GDELT MCP

> Stanford GDELT MCP gives you access to the world's largest open dataset for monitoring global news media coverage in real time. Track how stories spread across countries, languages, and political themes, analyzing everything from sentiment shifts to geographic hotspots. Use this MCP to turn raw global data into actionable intelligence for journalists, policy makers, and PR teams.

## Overview
- **Category:** data-analytics
- **Price:** Free
- **Tags:** gdelt, global-news, sentiment-analysis, media-monitoring, geopolitics, tv-news, multilingual

## Description

This connector links your AI client directly to the GDELT Project API, giving you a direct line to billions of records of global news coverage. You can monitor what's happening worldwide by analyzing articles across 100+ languages, tracking specific themes like climate change or elections, and seeing exactly where media attention is focused geographically. It’s far more than just searching; it helps you map the story itself—who reported it, how they felt about it (the tone), and which nations are paying attention. If you're relying on manual dashboards or basic search engines to track global events, this MCP changes that. Through Vinkius, you connect your agent once and gain instant access to this massive data catalog, letting your AI client do the heavy lifting of complex cross-cultural analysis for you.

## Tools

### get_geo_data
Retrieves geographic point data for news events, showing coordinates and location names for mapping.

### get_tv_channels
Lists available TV news channels to understand the scope of broadcast analysis.

### get_tv_timeline
Tracks how much airtime specific stories receive on television compared to general online articles.

### get_themes
Retrieves standardized GDELT themes, classifying news content across broad topics like politics or health.

### get_timeline_country
Shows the source country distribution timeline to identify which nations are covering an issue and when global interest grows.

### get_timeline_lang
Provides a language distribution timeline, showing how different linguistic communities cover a topic over time.

### get_timeline_tone
Generates a sentiment and tone timeline to track public opinion shifts related to any given subject.

### get_timeline_volume
Provides the news volume timeline, which tracks overall media attention spikes and helps understand a story's lifecycle.

### get_tone_chart
Generates a tone distribution chart to visualize if coverage is mostly positive, negative, or neutral.

### get_word_cloud
Extracts the dominant key terms and concepts associated with a topic in media coverage.

### search_articles
Searches global news articles using filters for date range, language, country, or general keywords.

### search_by_country
Narrows the search results to only include news published from a specific country code.

### search_by_language
Limits searches to articles written in one of over 100 specified language codes.

### search_by_theme
Filters news results based on standardized topic categories like ENVIRONMENT or ELECTION.

### search_nearby
Searches for articles where two specific terms appear near each other, providing a more precise search than keyword matching.

### search_tv
Finds and analyzes transcripts from major television news networks using keywords.

## Prompt Examples

**Prompt:** 
```
What are the latest news articles about AI regulation?
```

**Response:** 
```
I've searched GDELT for the latest articles on "AI regulation" from the past week. Coverage spans multiple countries with articles from the US, EU, UK, and China discussing frameworks, legislation, and safety standards.
```

**Prompt:** 
```
How has sentiment about climate change evolved over the last 3 months?
```

**Response:** 
```
I've retrieved the tone timeline for "climate change" over the past 3 months from GDELT. The sentiment shows fluctuations correlating with major climate events and policy announcements.
```

**Prompt:** 
```
Search for TV news clips mentioning quantum computing
```

**Response:** 
```
I've found TV news clips mentioning "quantum computing" from GDELT's TV news archive. Clips include segments from CNN, MSNBC, and Fox Business discussing breakthroughs and investment in quantum technology.
```

## Capabilities

### Map Global Coverage
Pinpoint news events and measure media focus using coordinates or heatmap overlays.

### Analyze Topic Trends Over Time
Track how the volume, sentiment, or language coverage of a specific subject changes over months or years.

### Identify Thematic Hotspots
Classify news content using hundreds of standardized themes covering politics, health, and economics.

### Deep Dive into Language/Region Spread
Determine which countries or linguistic communities are generating the most coverage on a topic.

### Monitor TV vs. Online Narrative Shifts
Compare how stories are covered in closed-captioned television news versus general online articles.

## Use Cases

### Tracking Political Fallout from an Election
A political analyst needs to know how different regions viewed a policy change. They use search_by_theme for 'ELECTION' and then run get_timeline_country to visualize which nations escalated their coverage and when that interest became global.

### Monitoring Brand Reputation During a Crisis
A PR manager needs immediate insight. They use get_tone_chart on the company name over the last 3 months, seeing if negative sentiment spikes correlate with specific geographic areas identified by get_geo_data.

### Researching Environmental Policy Spread
A policy expert wants to see how climate concerns are framed. They use search_by_theme for 'ENV_CLIMATECHANGE' and then utilize get_timeline_lang to compare coverage differences between English, Mandarin, and Spanish sources.

### Investigating Complex Scientific Claims
A journalist needs to find nuanced reporting. Instead of just searching for 'vaccine side effects,' they use search_nearby with the terms 'vaccine' and 'adverse event' plus a distance parameter, ensuring the context is right.

## Benefits

- Track the life cycle of a story. Instead of just seeing one article, you use get_timeline_volume to see exactly when media attention spikes and how long that coverage lasts.
- Understand global context instantly. Use search_by_country or search_by_language to filter results immediately, letting you compare US vs. UK coverage on the same issue.
- Measure emotional impact precisely. get_timeline_tone tracks sentiment shifts over time, so you know if public opinion is cooling down or heating up around a topic.
- Go beyond keywords. Use search_nearby when you need to find articles where two specific terms appear close together, which is much more accurate than a basic keyword query.
- Compare media types. You can run get_tv_timeline and compare the volume of coverage on broadcast news versus what's available in general online archives.

## How It Works

The bottom line is, you tell your agent what global insight you need, and it handles the complex data calls to retrieve the precise information.

1. Subscribe to this MCP on Vinkius, which connects your AI client to the entire GDELT Project API.
2. Your agent accesses the structured tools and functions necessary for global data queries. No API key is needed because the connection is managed by Vinkius.
3. You prompt your AI client with a complex request—like 'Show me how sentiment around AI regulation changed in Europe over 6 months'—and the MCP executes the required steps across multiple data points.

## Frequently Asked Questions

**Can I track sentiment over time using Stanford GDELT MCP?**
Yes. Use get_timeline_tone. This tool tracks how public opinion changes around a topic, providing positive and negative values so you can monitor shifts in brand reputation or political favor.

**Does Stanford GDELT MCP support keyword searching across multiple languages?**
Yes. You use search_articles by combining language codes (like 'english' and 'spanish') with a date range to gather global coverage on the same issue.

**How do I find out which country is most interested in an issue?**
Use get_timeline_country. This tool analyzes source country distribution, showing you exactly when and where media attention for a given topic originates globally.

**Is Stanford GDELT MCP only for major news outlets?**
No. It covers global news sources across 100+ languages and includes specialized searches like search_tv, which pulls transcripts from networks such as CNN and BBC.

**What is the best way to compare two topics in GDELT?**
Start by using get_timeline_volume for both topics over the same time frame. This lets you graph their attention spikes against each other, revealing correlation or divergence.