Hiasynth Humanity — European demographics & market data

[Hiasynth Humanity]

Hiasynth Humanity

Query humanity.

Find your customers, size your market and choose your next location anywhere in Europe, by asking your AI assistant in plain words. See who lives around each neighbourhood you're considering, how many people match your customer and the towns where they cluster most, and which channels reach them, from a whole country down to 1 km².

Every answer is computed from Hiasynth Humanity, a modelled population of all 522 million Europeans: each person in a household, each household in its neighbourhood, with about 700 attributes per person, from age and income to values, media habits and what the household spends. So you can combine anything, in any place.

[Add to Claude] [Add to Cursor] [Install in VS Code] [Install in VS Code Insiders]

https://mcp.hiasynth.co

Streamable HTTP · OAuth 2.1 (sign in with your Hiasynth account) or an API key · every tool read-only.

Connect

Headless clients send an API key from hiasynth.co/app/mcp as Authorization: Bearer …:

{  "mcpServers": {    "hiasynth": {      "url": "https://mcp.hiasynth.co",      "headers": { "Authorization": "Bearer YOUR_API_KEY" }    }  }}

Build it into your product

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):

{  "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"}

Full reference: Build with Hiasynth.

Try asking

• 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

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.

Links

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This repository holds the public listing files (registry server.json, Gemini CLI extension). The server itself is operated by Hiasynth AB.