The data layer
for AI workflows
Give your AI agent real-world context. The DataGarden connects to any LLM or agent framework via Model Context Protocol — delivering live economic, demographic, and geo data with a single natural-language request.
datagarden.query("Which regions in Germany had the fastest household income growth in the last 3 years, ranked by NUTS-2?")
"regions": [
{ "nuts2": "DE21", "name": "Oberbayern", "growth_pct": +14.2 },
{ "nuts2": "DE71", "name": "Darmstadt", "growth_pct": +12.8 },
{ "nuts2": "DE11", "name": "Stuttgart", "growth_pct": +11.5 }
],
"source": "Eurostat",
"updated": "2025-Q4",
"coverage": "38 NUTS-2 regions"
}
Built on open standards, trusted by data-driven builders
Your AI agent is data-blind
LLMs are trained on general knowledge. But intelligence about specific regions, demographics, and economic conditions is locked away — scattered across government portals, paywalled databases, and incompatible formats.
No real-world context
Your agent can reason brilliantly but can't tell you which district has the highest disposable income or where your demographic actually lives — because it doesn't have access to that data.
Integration hell
Building data pipelines for each government data source takes months. Each country, each agency, each dataset format requires a custom scraper, cleaner, and parser — before you write a single line of AI logic.
Data goes stale
Public datasets are released sporadically. Keeping up with quarterly Eurostat updates, annual census releases, and monthly economic indicators is a full-time job — not a feature your team should be building.
From question to insight in milliseconds
Three steps. No pipeline. No schema to learn. Your agent speaks — The DataGarden answers.
Connect via MCP
Add The DataGarden MCP server to your agent framework. Works with Claude, GPT-4, Gemini, LangChain, CrewAI, AutoGen, and any MCP-compatible runtime. One config line — done.
{
"mcpServers": {
"datagarden": {
"url": "https://mcp.the-datagarden.io",
"apiKey": "YOUR_KEY"
}
}
}
Ask in plain language
Your agent queries The DataGarden using natural language or structured tool calls. No SQL. No schema documentation. Our semantic layer translates intent to data automatically.
tool_result = datagarden.query(
"retail spending per capita in
Belgian provinces, 2022-2024"
)
Get structured, cited data
Receive clean JSON with source citations, update timestamps, and geographic context. Your agent can reason over it, visualize it, or pass it directly to your application — no cleaning needed.
{
"data": [...],
"source": "NBB / Statbel",
"period": "2022-2024",
"geo_level": "NUTS-2",
"confidence": "high"
}
What your agent can ask
Real queries. Real responses. See how The DataGarden answers complex geographic and economic questions in plain English.
{
"query_intent": "demographic + income filter, NUTS-3 France",
"results": [
{ "nuts3": "FR101", "name": "Paris", "pop_25_44_pct": 34.1, "hh_income": 41200 },
{ "nuts3": "FRJ11", "name": "Hérault", "pop_25_44_pct": 31.8, "hh_income": 34800 },
// ... 6 more regions
],
"sources": ["Eurostat NUTS-3", "INSEE"],
"updated": "2024-Q3",
"total_qualifying": 23
}
Everything your agent needs to understand the world
247 curated public data sources. 195+ countries. 15 thematic categories. Updated automatically.
Geographic intelligence
NUTS-1 to NUTS-3 granularity across Europe. H3 hex-grid indexing for precise location context. Seamlessly query regions, cities, and postal codes in a single call.
Demographics & population
Age structure, household size, education level, urbanization rates, and migration patterns at regional granularity. Sourced from Eurostat, national statistics offices, and World Bank.
Economic indicators
GDP, household income, disposable income, consumer spending, retail sales, and business density. Track growth, compare regions, identify emerging markets.
Retail & market data
Store density, category penetration, footfall proxies, and competitor presence by location. Purpose-built for FMCG, retail, and distribution strategy agents.
Environmental & climate
Temperature patterns, precipitation, air quality indices, and climate risk scores. Essential for agriculture, insurance, sustainability, and energy sector agents.
Always up to date
Automatic ingestion of new data releases from all 247 sources. No stale caches. Your agent always reasons over the most recent published data — without you lifting a finger.
MCP-native from day one
Model Context Protocol is the emerging industry standard for connecting AI agents to data and tools. Anthropic open-sourced MCP and it was donated to the Linux Foundation AI & Data Foundation in December 2025 — making it a permanent, neutral standard.
Model Context Protocol (MCP)
Industry standard for AI-to-data connectivity. 97M+ monthly SDK downloads.
REST API fallback
Full REST API for teams not yet using MCP. Same data, same latency, OpenAPI spec included.
Data coverage by theme
Demographics
Age, gender, household, migration
Economics
GDP, income, employment, trade
Retail & FMCG
Stores, categories, market share
Education
Attainment, enrollment, outcomes
Health
Life expectancy, disease rates
Environment
Climate, energy, emissions
Infrastructure
Transport, broadband, logistics
Public finance
Tax, spending, debt by region
Built for the builders shaping AI
From fast-moving startups to enterprise AI teams — The DataGarden powers agents that need to know the world.
Sales territory optimization agent
An autonomous sales planning agent needs to identify the top 20 expansion districts for a new product launch — considering income, competition density, and population age profile simultaneously.
Outcome
Agent queries DataGarden for multi-variable regional ranking. Produces a ranked territory list with rationale in under 2 minutes — a task that previously took a analyst 2 weeks and a data engineering sprint.
const territories = await datagarden.query({
question: "Top 20 NUTS-3 regions in Spain for premium pet food launch",
filters: {
income_min: 35000,
age_25_54_pct_min: 28,
pet_ownership_proxy: "high"
}
});
// → 20 ranked regions, cited, ready to act on
Location intelligence platform
A B2B SaaS product needs to enrich user-submitted locations with rich socioeconomic context — instantly and at scale. Users drop a pin; the platform explains the neighborhood.
Outcome
DataGarden API enriches each location with 40+ data points in under 100ms. No data warehouse. No ETL. The product team ships geo-intelligence features in days instead of quarters.
const context = await datagarden.enrich({
lat: 48.8566,
lng: 2.3522,
themes: ["demographics", "economics", "retail"]
});
// → {population, income, age_profile, stores_nearby, ...}
Automated market research reports
A strategy consultancy builds an AI system to auto-generate market entry reports. It needs data on demographics, consumer spending, competitor presence, and regulatory context for any European market.
Outcome
DataGarden feeds the research agent with consistent, citable data across all 195+ countries. What used to take a junior analyst 3 days now takes the agent 8 minutes, with full source citations for the report.
const report = await datagarden.compare({
question: "Market attractiveness for organic food across Benelux NUTS-2",
output: "ranked_table",
include_citations: true
});
// → Fully cited, ready for report inclusion
Start free. Scale as you build.
Usage-based pricing that grows with your product. No data engineering costs. No procurement cycles.
- 1,000 queries/month
- All 15 data themes
- MCP + REST access
- 5 countries included
- Community support
- 10,000 queries/month
- 195+ countries
- All 15 data themes
- MCP + REST + webhook
- Custom geo filters
- Email + Slack support
- SLA: 99.5% uptime
- Unlimited queries
- Dedicated data pipelines
- Custom data ingestion
- White-label options
- Data residency options
- Dedicated CSM
- Custom SLA
All plans include MCP and REST API access · No setup fees · Cancel anytime · Usage overages at €0.03/query
Common questions
What is Model Context Protocol (MCP)?
MCP is an open standard developed by Anthropic that allows AI agents to securely connect to external data sources and tools. It's now maintained by the Linux Foundation AI & Data Foundation, with 97M+ monthly SDK downloads and support from all major AI providers. The DataGarden implements MCP natively, so your agent gets access to real-world data without custom integration code.
Which agent frameworks are compatible?
Any framework that supports MCP: Claude (Anthropic), OpenAI assistants, LangChain, LlamaIndex, CrewAI, AutoGen, and custom agents using the MCP SDK. We also provide a REST API for frameworks that don't yet support MCP natively.
How fresh is the data?
Data is automatically refreshed when source agencies publish updates. Most economic indicators update quarterly; demographic data updates annually; environmental and some retail data updates more frequently. Every response includes an updated field with the exact publication date of each source.
Can I query in languages other than English?
Yes. The DataGarden's semantic layer understands queries in all major European languages including French, German, Spanish, Dutch, Italian, and Polish. Responses are always returned in structured JSON with English field names for consistency.
Is the data licensed for commercial use?
All data in The DataGarden is sourced from public sector bodies with open data licenses (Eurostat, World Bank, OECD, national statistics offices). Our platform license allows commercial use. Every response includes source citations so your downstream use cases remain transparent.
What if I need data not yet in the catalog?
We onboard new sources on request. On the Scale/Enterprise plan, we can ingest custom data pipelines within agreed timelines. Contact us with your requirements and we'll assess feasibility within 48 hours.
Give your AI agent the world's data
Connect in minutes. Query in plain language. Build products that actually know where they are.