Stanford GDELT MCP Server for Pydantic AIGive Pydantic AI 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

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Stanford GDELT through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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

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python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Stanford GDELT "
            "(16 tools)."
        ),
    )

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

asyncio.run(main())

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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.

Pydantic AI validates every Stanford GDELT tool response against typed schemas, catching data inconsistencies at build time. Connect 16 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic 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 Pydantic AI

When Pydantic 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

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 Pydantic AI via MCP

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

Install Pydantic AI

Run pip install pydantic-ai

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 with type-safe schemas

Why Use Pydantic AI with the Stanford GDELT MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Stanford GDELT integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Stanford GDELT connection logic from agent behavior for testable, maintainable code

Stanford GDELT + Pydantic AI Use Cases

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

Type-safe data pipelines: query Stanford GDELT with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Stanford GDELT tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Stanford GDELT and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Stanford GDELT responses and write comprehensive agent tests

Example Prompts for Stanford GDELT in Pydantic AI

Ready-to-use prompts you can give your Pydantic 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Stanford GDELT + Pydantic AI FAQ

Common questions about integrating Stanford GDELT MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Stanford GDELT MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.

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