Stanford GDELT MCP Server for LangChainGive LangChain instant access to 16 tools to Get Geo Data, Get Themes, Get Timeline Country, and more
GDPR Free for SubscribersLangChain is the leading Python framework for composable LLM applications. Connect Stanford GDELT through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this MCP Server for LangChain
The Stanford GDELT MCP Server for LangChain 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 asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"stanford-gdelt": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Stanford GDELT, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(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.
LangChain's ecosystem of 500+ components combines seamlessly with Stanford GDELT through native MCP adapters. Connect 16 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
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 LangChain 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 LangChain
When LangChain 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 LangChain via MCP
Follow these steps to wire Stanford GDELT into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Stanford GDELT MCP Server
LangChain provides unique advantages when paired with Stanford GDELT through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Stanford GDELT MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Stanford GDELT queries for multi-turn workflows
Stanford GDELT + LangChain Use Cases
Practical scenarios where LangChain combined with the Stanford GDELT MCP Server delivers measurable value.
RAG with live data: combine Stanford GDELT tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Stanford GDELT, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Stanford GDELT tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Stanford GDELT tool call, measure latency, and optimize your agent's performance
Example Prompts for Stanford GDELT in LangChain
Ready-to-use prompts you can give your LangChain 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 LangChain
Common issues when connecting Stanford GDELT to LangChain through Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersStanford GDELT + LangChain FAQ
Common questions about integrating Stanford GDELT MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Explore More MCP Servers
View all →
Fera.ai
12 toolsManage reviews and social proof via Fera.ai — list customer feedback, track product ratings, and monitor UGC directly through your AI agent.
DatoCMS
10 toolsManage headless content via DatoCMS — execute GraphQL queries, handle record CRUD, manage content models, and audit media uploads directly from any AI agent.
Azure Functions Invoke
1 toolsThis MCP does exactly one thing: it invokes a single Azure Function. That's its only function, and nothing else. Incredible for letting your AI execute secure serverless compute.
Gainsight CS
12 toolsManage customer success, track health scores, and oversee the timeline via AI agents with Gainsight CS.