Stanford GDELT MCP. Map Global Media Events & Themes.
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.
Give Claude and any AI agent real-world access
Pinpoint news events and measure media focus using coordinates or heatmap overlays.
Track how the volume, sentiment, or language coverage of a specific subject changes over months or years.
Classify news content using hundreds of standardized themes covering politics, health, and economics.
Determine which countries or linguistic communities are generating the most coverage on a topic.
Compare how stories are covered in closed-captioned television news versus general online articles.
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What AI agents can do with Stanford GDELT: 16 Tools for Global Data
These tools let your agent perform highly specific data analysis, from mapping geographical hotspots to tracking sentiment changes over time.
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Start using Stanford GDELT MCPGet 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...
Get Themes
Retrieves standardized GDELT themes, classifying news content across broad topics...
Get Timeline Country
Shows the source country distribution timeline to identify which nations are...
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...
Get Tone Chart
Generates a tone distribution chart to visualize if coverage is mostly positive...
Get Word Cloud
Extracts the dominant key terms and concepts associated with a topic in media...
Search Articles
Searches global news articles using filters for date range, language, country, or...
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...
Search Nearby
Searches for articles where two specific terms appear near each other, providing a...
Search Tv
Finds and analyzes transcripts from major television news networks using keywords.
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Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Mapping Global Attention Spreads Is Hard
Right now, tracking a single story across the world means jumping through endless dashboards. You open one tab for US news, another for Chinese media reports, and yet another to check sentiment via a separate dashboard. Copying dates, filtering languages, and trying to manually correlate how coverage shifts from online articles to broadcast TV is tedious at best.
With this MCP, your agent handles the entire process. You give it the topic, and it executes multiple data calls—running get_timeline_volume for spikes, then getting_geo_data for locations, and finally using search_by_theme to categorize the whole narrative. You get a single, synthesized answer, not three dozen links.
Analyze Coverage With The GDELT Theme Distribution
Manually analyzing themes across different articles is nearly impossible. You'd have to read hundreds of headlines and manually categorize them into 'ECONOMICS,' 'HEALTH,' or 'PROTEST.' This requires specialized human labor just for categorization.
Now, you use get_themes. Your agent runs the classification against massive datasets, instantly telling you that 40% of the coverage falls under 'ECONOMIC_BANKRUPTCY' and 25% is tagged as 'HEALTH_PANDEMIC.' You skip the reading and go straight to the insights.
What Stanford GDELT MCP does for your AI
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.
019dea60-babd-7187-9a34-7f464919a360 How to set up Stanford GDELT MCP
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.
Subscribe to this MCP on Vinkius, which connects your AI client to the entire GDELT Project API.
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.
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.
Who uses Stanford GDELT MCP
Journalists and political analysts who get tired of sifting through siloed dashboards. If you need to know why a story is breaking now—and where its attention originated—you need this.
Needs to track the initial reporting of an event, tracing coverage from small local papers to major international outlets by using search_articles and get_geo_data.
Must monitor brand reputation in real time. They use get_timeline_tone and get_tone_chart to track sentiment shifts across different regions after a major announcement.
Analyzes geopolitical discourse, using search_by_theme and get_themes to understand how standardized topic categories (like TERROR or PROTEST) are being covered globally.
Benefits of connecting Stanford GDELT MCP
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.
Stanford GDELT MCP 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.
Stanford GDELT MCP tradeoffs
What to watch out for, and the recommended way to handle each one.
Treating it like Google Search
A user searches for 'China economy' in their AI client and expects a list of recent headlines. They get raw data, but no context on how the story is spreading.
Use search_articles with filters to narrow down the scope (e.g., only from China) and follow up by running get_timeline_country or get_themes to give that headline historical and contextual depth.
Focusing only on keywords
A user searches for 'conflict' and gets thousands of articles, but can’t tell if the tone was positive (de-escalating) or negative (violence).
Always follow a general search with get_timeline_tone. This gives you the emotional layer—the actual feeling behind the words—which is crucial for analysis.
Missing geographic context
A user sees an article about 'water scarcity' and assumes it applies everywhere, missing which specific nations are most affected or reporting on it.
Always run get_geo_data immediately after a search. This provides coordinates and location names, helping you pinpoint the exact source of the media attention.
When to use Stanford GDELT MCP
Use this MCP if your core problem is measuring global scale or tracking how attention moves—whether that's political focus, emotional tone, or journalistic coverage. You need to know not just what was said, but where, when, and by whom. Don’t use this if you simply need basic keyword searching; those simple searches are better handled by generic web search tools. Similarly, don't rely on it if your goal is only localized data (e.g., just California news). If you're restricted to one country or one language for the entire analysis, then a simpler, local API might suffice. But because you need cross-border comparison—comparing US coverage of 'migration' with EU coverage of 'migration'—this MCP’s breadth is what you require.
Frequently asked questions about Stanford GDELT MCP
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.