Google makes real-world information extra accessible to AI — and coaching pipelines will adore it

Google is turning its vast public data trove into a goldmine for AI with the debut of the Data Commons Model Context Protocol (MCP) Server — enabling developers, data scientists, and AI agents to access real-world statistics using natural language and better train AI systems.

Launched in 2018, Google’s Data Commons organizes public datasets from a range of sources, including government surveys, local administrative data, and statistics from global bodies such as the United Nations. With the release of the MCP Server, this data is now accessible via natural language, allowing developers to integrate it into AI agents or applications.

AI systems are often trained on noisy, unverified web data. Combined with their tendency to “fill in the blanks” when sources are lacking, this leads to hallucinations. As a result, companies looking to fine-tune AI systems for specific use cases often need access to large, high-quality datasets. By publicly releasing the MCP Server for its Data Commons, Google aims to tackle both challenges.

Data Commons’ new MCP server bridges public datasets — from census figures to climate statistics — with AI systems that increasingly depend on accurate, structured context. By making this data accessible via natural language prompts, the release aims to ground AI in verifiable, real-world information.

“The Model Context Protocol is letting us use the intelligence of the large language model to pick the right data at the right time, without having to understand how we model the data, how our API works,” said Google Data Commons head Prem Ramaswami in an interview.

First introduced by Anthropic last November, MCP is an open industry standard that enables AI systems to access data from various sources, including business tools, content repositories, and app development environments, providing a common framework for understanding contextual prompts. Since its launch, companies such as OpenAI, Microsoft, and Google have adopted the standard for integrating their AI models with various data sources.

While other tech companies explored how to apply the standard to their AI models, Ramaswami and his team at Google began investigating how the framework could be used to make the Data Commons platform more accessible earlier this year.

Google has also partnered with the ONE Campaign, a nonprofit organization focused on improving economic opportunities and public health in Africa, to launch the One Data Agent. This AI tool utilizes the MCP Server to surface tens of millions of financial and health data points in plain language.

The ONE Campaign approached Google’s Data Commons team with a prototype implementation of MCP on its own custom server. That interaction, Ramaswami told TechCrunch, was the turning point that led the team to build a dedicated MCP Server in May.

However, the experience is not limited to the ONE Campaign. The open nature of the Data Commons MCP Server makes it compatible with any LLM, and Google has provided several ways for developers to get started. A sample agent is available through the Agent Development Kit (ADK) in a Colab notebook, and the server can also be accessed directly via the Gemini CLI or any MCP-compatible client using the PyPI package. Example code is also provided on a GitHub repository.