Hiasynth Humanity — European demographics & market data
Official
by hi-synth
Europe's population layer: demographics, spending and market data for every place, down to 1 km².
Model Context Protocol (MCP) Server: co.hiasynth/humanity
The co.hiasynth/humanity MCP server provides Europe-focused population layer data, covering demographics, spending, and market data at a fine granularity (down to 1 km²). The server is described as a dataset layer for “every place,” intended to support location-level analysis.
🛠️ Key Features
Population layer for Europe
Demographics data
Spending data
Market data
Spatial granularity down to 1 km²
🚀 Use Cases
Building place-level demographic profiles across Europe
Combining demographic and spending signals for market analysis
Supporting location-specific market segmentation and planning
⚡ Developer Benefits
Consistent access to Europe population layer data
Developer-facing integration via an MCP server
⚠️ Limitations
Scope limited to Europe
Data granularity and fields are specified only as demographics, spending, and market data (no additional data categories provided)
Europe's population layer. Demographic, spending and market data for every place in Europe, as one remote MCP server, for your AI assistant or inside your own product.
Nobody could say what a Lyon neighbourhood spends on restaurants, or how many renting families in Bavaria earn above the median. Statistics come as national averages, surveys as small samples, and nothing ties who people are to where they live and what they spend. Hiasynth Humanity models it: all 522 million Europeans, each in a household, each household in its neighbourhood, with a line-by-line budget. That's ~700 attributes per person, queryable in any combination, at any resolution down to 1 km². No real person is in it, so it's private by design.
The same server is an API for your features and agents: size a market inside a CRM and show where to dig next, tie a marketing tool's personas to the actual population, or show the wallet size of any neighbourhood.
Auth: OAuth 2.1 for user-facing apps, or a server-side API key from hiasynth.co/app/mcp (Authorization: Bearer …).
Responses: structured JSON with the numbers already computed: counts, shares, lift against the local baseline, top places, and a confidence tier. Aggregates only, never individual records.
Fine control:query_population takes a structured spec (group-by, filters, count/share/avg) for pipelines that need exact shapes.
Any MCP client library works: the official SDKs, the Claude and OpenAI APIs' MCP connectors, LangChain, or plain JSON-RPC over HTTPS.
What analyze_market returns for renting families with above-median income in Bavaria (trimmed):
json
{"headline":"418,921 households (6.81% of Bayern). Biggest markets: München, Nürnberg.","segment_size":{"count":418921,"share_pct":6.81,"population":6153349},"core_markets":[{"name":"München, Landeshauptstadt","count":43674,"local_share_pct":6.51,"lift":0.96},{"name":"Würzburg","count":5272,"local_share_pct":8.41,"lift":1.23}],"hotspots":[{"name":"Sonthofen, St","count":1111,"local_share_pct":10.56,"lift":1.5}],"confidence_tier":"medium","computed":"3 attribute filter(s) over 6,153,349 households at municipality resolution; counted, not estimated"}
How many renting families with above-median income live in Bavaria, and where do they cluster?
What do households in La Croix-Rousse, Lyon, spend on restaurants and cafés?
Who lives in Södermalm, Stockholm — and how is it different from the rest of Sweden?
Which media channels reach women aged 25–40 in the top income quartile in France?
Compare Munich, Hamburg and Vienna for affluent 30–45 year olds.
What does a typical household in Porto spend on eating out, and how does its carbon footprint compare?
Tools
Tool
What it answers
analyze_market
How many people match a segment, who they are, and where they cluster.
describe_area
Who lives in a place — demographics, values, economics, media, spending, footprint.
reach_audience
Which channels, values and brands over-index for a segment.
compare_places
Side-by-side comparison of places for one segment.
rank_places
Rank regions or towns for a segment.
sample_persona
Representative personas (statistical composites, never a real row).
resolve_place
Turn a name, code, postcode or point into the exact area used.
query_population
Structured group-by for agent builders.
list_attributes
Search the attribute catalogue.
All tools are read-only (readOnlyHint: true), return aggregates only, and suppress any cell under the privacy floor. Special-category attributes are never broken down below region level.
