Stanford GDELT MCP Server for Mastra AIGive Mastra AI instant access to 16 tools to Get Geo Data, Get Themes, Get Timeline Country, and more
GDPR Free for SubscribersMastra AI is a TypeScript-native agent framework built for modern web stacks. Connect Stanford GDELT through Vinkius and Mastra agents discover all tools automatically. type-safe, streaming-ready, and deployable anywhere Node.js runs.
Ask AI about this MCP Server for Mastra AI
The Stanford GDELT MCP Server for Mastra AI is a standout in the Data Analytics category — giving your AI agent 16 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
Geminiimport { Agent } from "@mastra/core/agent";
import { createMCPClient } from "@mastra/mcp";
import { openai } from "@ai-sdk/openai";
async function main() {
// Your Vinkius token. get it at cloud.vinkius.com
const mcpClient = await createMCPClient({
servers: {
"stanford-gdelt": {
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
},
});
const tools = await mcpClient.getTools();
const agent = new Agent({
name: "Stanford GDELT Agent",
instructions:
"You help users interact with Stanford GDELT " +
"using 16 tools.",
model: openai("gpt-4o"),
tools,
});
const result = await agent.generate(
"What can I do with Stanford GDELT?"
);
console.log(result.text);
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Stanford GDELT MCP Server
Connect to the GDELT Project API — the world's largest open platform for monitoring global news media in real time.
Mastra's agent abstraction provides a clean separation between LLM logic and Stanford GDELT tool infrastructure. Connect 16 tools through Vinkius and use Mastra's built-in workflow engine to chain tool calls with conditional logic, retries, and parallel execution. deployable to any Node.js host in one command.
What you can do
- Article Search — Search global news articles with filters for language, country, date range, and topic
- Volume Timelines — Track how media attention to any topic changes over time
- Sentiment Analysis — Monitor tone and sentiment shifts in coverage of any subject
- Geographic Mapping — Visualize where news events are happening around the world
- TV News Search — Search closed caption transcripts from CNN, Fox News, MSNBC, BBC, and more
- Theme Analysis — Explore standardized GDELT themes across geopolitics, health, environment, and economics
- Language Distribution — See which linguistic communities are covering a topic
- Country Distribution — Identify which nations produce the most coverage of specific issues
- Proximity Search — Find articles where two terms appear near each other
- Word Clouds — Extract dominant terms and concepts from coverage
The Stanford GDELT MCP Server exposes 16 tools through the Vinkius. Connect it to Mastra AI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 16 Stanford GDELT tools available for Mastra AI
When Mastra AI connects to Stanford GDELT through Vinkius, your AI agent gets direct access to every tool listed below — spanning gdelt, global-news, sentiment-analysis, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.
Get geo data on Stanford GDELT
Each point includes coordinates, location name, and article metadata. Use modes: "PointData" for individual points, "PointHeat" for heatmap data. Get geographic point data for news events
Get themes on Stanford GDELT
GDELT uses hundreds of themes from politics, economics, health, environment, technology, and more to classify news content. Get GDELT theme distribution for a topic
Get timeline country on Stanford GDELT
Reveals geographic patterns in media attention, identifies when a story goes global, and shows which nations are most interested in specific issues. Get source country distribution timeline
Get timeline lang on Stanford GDELT
Reveals which linguistic communities are paying attention to an issue and when interest spreads across language barriers. Get language distribution timeline for a topic
Get timeline tone on Stanford GDELT
Positive values indicate positive coverage, negative values indicate negative coverage. Essential for tracking public opinion shifts, crisis communications, and brand reputation monitoring. Get sentiment and tone timeline for a topic
Get timeline volume on Stanford GDELT
Essential for tracking media attention, identifying news spikes, and understanding the lifecycle of a story. Default timespan is 3 months. Get news volume timeline for any topic
Get tone chart on Stanford GDELT
Shows whether coverage is predominantly positive, negative, or neutral, and the overall emotional intensity of the coverage. Get tone distribution chart for a topic
Get tv channels on Stanford GDELT
Use this to understand the scope of TV news coverage available for analysis. Get available TV news channels inventory
Get tv timeline on Stanford GDELT
Reveals which stories dominate TV airtime and how TV coverage patterns differ from online news. Get TV news mention volume timeline
Get word cloud on Stanford GDELT
Reveals the dominant themes, entities, and concepts associated with a topic in media discourse. Get word cloud data showing key terms for a topic
Search articles on Stanford GDELT
Returns article titles, URLs, dates, source domains, languages, and source countries. Use timespan like "1d" (1 day), "1w" (1 week), "3m" (3 months). Use sourcelang codes like "english", "spanish", "portuguese", "french", "chinese", "arabic". Use sourcecountry codes like "US", "BR", "UK", "FR", "DE". Search global news articles across 100+ languages
Search by country on Stanford GDELT
Country codes follow ISO 2-letter format: US (United States), BR (Brazil), UK (United Kingdom), FR (France), DE (Germany), CN (China), JP (Japan), IN (India), RU (Russia), AU (Australia), CA (Canada), etc. Essential for understanding country-specific media perspectives on global events. Search news articles from a specific country
Search by language on Stanford GDELT
Covers 100+ languages. Language codes include: english, spanish, portuguese, french, german, italian, chinese, japanese, korean, arabic, russian, hindi, turkish, dutch, swedish, polish, and many more. Essential for monitoring how different linguistic communities cover the same event. Search news articles in a specific language
Search by theme on Stanford GDELT
Themes are standardized topic categories like TAX_FNCACT (financial actions), HEALTH_PANDEMIC, ENV_CLIMATECHANGE, TERROR, PROTEST, ELECTION, ECON_BANKRUPTCY, etc. Use this for precise topic-based monitoring. Search articles by GDELT standardized theme
Search nearby on Stanford GDELT
More precise than simple keyword search. Use distance parameter to control proximity (default 10 words). Example: term1="climate", term2="migration", distance=15. Search articles where two terms appear near each other
Search tv on Stanford GDELT
Returns clips with timestamps, station names, transcript snippets, and video preview URLs. Covers CNN, Fox News, MSNBC, BBC, and more. Modes: "ClipGallery" for clips, "StationChart" for station comparison. Search TV news transcripts by keyword
Connect Stanford GDELT to Mastra AI via MCP
Follow these steps to wire Stanford GDELT into Mastra AI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
npm install @mastra/core @mastra/mcp @ai-sdk/openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
agent.ts and run with npx tsx agent.tsExplore tools
Why Use Mastra AI with the Stanford GDELT MCP Server
Mastra AI provides unique advantages when paired with Stanford GDELT through the Model Context Protocol.
Mastra's agent abstraction provides a clean separation between LLM logic and tool infrastructure. add Stanford GDELT without touching business code
Built-in workflow engine chains MCP tool calls with conditional logic, retries, and parallel execution for complex automation
TypeScript-native: full type inference for every Stanford GDELT tool response with IDE autocomplete and compile-time checks
One-command deployment to any Node.js host. Vercel, Railway, Fly.io, or your own infrastructure
Stanford GDELT + Mastra AI Use Cases
Practical scenarios where Mastra AI combined with the Stanford GDELT MCP Server delivers measurable value.
Automated workflows: build multi-step agents that query Stanford GDELT, process results, and trigger downstream actions in a typed pipeline
SaaS integrations: embed Stanford GDELT as a first-class tool in your product's AI features with Mastra's clean agent API
Background jobs: schedule Mastra agents to query Stanford GDELT on a cron and store results in your database automatically
Multi-agent systems: create specialist agents that collaborate using Stanford GDELT tools alongside other MCP servers
Example Prompts for Stanford GDELT in Mastra AI
Ready-to-use prompts you can give your Mastra AI agent to start working with Stanford GDELT immediately.
"What are the latest news articles about AI regulation?"
"How has sentiment about climate change evolved over the last 3 months?"
"Search for TV news clips mentioning quantum computing"
Troubleshooting Stanford GDELT MCP Server with Mastra AI
Common issues when connecting Stanford GDELT to Mastra AI through Vinkius, and how to resolve them.
createMCPClient not exported
npm install @mastra/mcpStanford GDELT + Mastra AI FAQ
Common questions about integrating Stanford GDELT MCP Server with Mastra AI.
How does Mastra AI connect to MCP servers?
MCPClient with the server URL and pass it to your agent. Mastra discovers all tools and makes them available with full TypeScript types.Can Mastra agents use tools from multiple servers?
Does Mastra support workflow orchestration?
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