Turnono Datacommons Mcp Server
Discover public statistical indicators in Data Commons and pull observations while scoping data-heavy products or reports.
Overview
ai.smithery/turnono-datacommons-mcp-server is a MCP server for the Idea phase that lets agents discover and query Data Commons statistical indicators and observations.
What is this MCP server?
- Discover statistical indicators and topics in Google Data Commons
- Retrieve observations for specific variables via MCP tools
- Smithery-hosted streamable-http remote (v1.16.0)
- Open source at github.com/turnono/datacommons-mcp-server
- Bearer Smithery auth for managed remote access
- Smithery package version 1.16.0
- Remote endpoint server.smithery.ai/@turnono/datacommons-mcp-server/mcp
- GitHub repository github.com/turnono/datacommons-mcp-server
What problem does it solve?
You waste days hunting trustworthy public statistics manually when an agent could traverse Data Commons if it had structured MCP access.
Who is it for?
Builders researching civic, economic, or demographic datasets for content products, maps, or evidence-backed pitches.
Skip if: Teams that need proprietary warehouse ETL, sub-second analytics on private data, or guaranteed coverage of every national statistical office.
What do I get? / Deliverables
After setup, your agent can list indicators, explore topics, and pull observations to inform scope, copy, and early data models.
- Indicator and topic discovery through agent tool calls
- Observation pulls for scoped places and variables
- Research notes and specs backed by retrievable public statistics
Recommended MCP Servers
Journey fit
First appears in Idea research when you map credible public statistics before committing to datasets, charts, or GEO content. Research subphase covers indicator discovery and topic exploration—not yet ETL implementation in your repo.
How it compares
Public statistics MCP bridge, not a spreadsheet skill or a managed BI warehouse.
Common Questions / FAQ
Who is ai.smithery/turnono-datacommons-mcp-server for?
Indie founders and analysts who want MCP agents to explore and retrieve Data Commons indicators during product and content research.
When should I use ai.smithery/turnono-datacommons-mcp-server?
Use it early when choosing which public metrics to cite, prototype, or embed before you invest in custom data pipelines.
How do I add ai.smithery/turnono-datacommons-mcp-server to my agent?
Add the Smithery remote https://server.smithery.ai/@turnono/datacommons-mcp-server/mcp with Bearer {smithery_api_key} in your MCP client configuration.