Stanford GDELT MCP Server for LlamaIndexGive LlamaIndex instant access to 16 tools to Get Geo Data, Get Themes, Get Timeline Country, and more

Name: Stanford GDELT
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Stanford GDELT as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Ask AI about this MCP Server for LlamaIndex

Open in ChatGPT Open in Claude Open in Perplexity

The Stanford GDELT MCP Server for LlamaIndex is a standout in the Data Analytics category — giving your AI agent 16 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

python

import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to Stanford GDELT. "
            "You have 16 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in Stanford GDELT?"
    )
    print(response)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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.

LlamaIndex agents combine Stanford GDELT tool responses with indexed documents for comprehensive, grounded answers. Connect 16 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex

When LlamaIndex 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

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

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

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

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

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

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

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

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

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

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 LlamaIndex via MCP

Follow these steps to wire Stanford GDELT into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 16 tools from Stanford GDELT

Why Use LlamaIndex with the Stanford GDELT MCP Server

LlamaIndex provides unique advantages when paired with Stanford GDELT through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Stanford GDELT tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Stanford GDELT tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Stanford GDELT, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Stanford GDELT tools were called, what data was returned, and how it influenced the final answer

Stanford GDELT + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the Stanford GDELT MCP Server delivers measurable value.

Hybrid search: combine Stanford GDELT real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Stanford GDELT to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Stanford GDELT for fresh data

Analytical workflows: chain Stanford GDELT queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for Stanford GDELT in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

Common issues when connecting Stanford GDELT to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Stanford GDELT + LlamaIndex FAQ

Common questions about integrating Stanford GDELT MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Stanford GDELT tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.

Does LlamaIndex support async MCP calls?

Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.

Explore More MCP Servers

View all →

ReciPal

4 tools

Manage food recipes — audit ingredients and nutrition via AI.

Doofinder

10 tools

Empower e-commerce search via Doofinder — perform keyword and filtered searches, get predictive suggestions, and audit search analytics directly from any AI agent.