How can I access and query the data?

You can execute standard SQL SELECT queries directly, integrate via its RESTful HTTP API, or utilize progressive context loading for schema and metadata on demand, making integration flexible.

What is Indian Analytics?

Indian Analytics is a Model Context Protocol (MCP) server that provides weighted Indian consumer analytics data stored in PostgreSQL, designed for in-depth demographic and behavioral insights.

How does Indian Analytics ensure accurate population representation?

It ensures accuracy by automatically applying population weights to all user counts and performing NCCS (New Consumer Classification System) merging, allowing for analysis that precisely represents India's socioeconomic classes.

What types of insights can I gain from the data?

You can analyze mobile app usage, detailed user demographics (age, gender, NCCS class, region), and other behavioral metrics. All data is automatically weighted to provide accurate population-level insights.

Indian Analytics

Name: Indian Analytics
Author: adityac7

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Provides access to Indian consumer analytics data stored in PostgreSQL, offering weighted analysis and socioeconomic class merging via a Model Context Protocol server.

The Indian Analytics tool functions as a Model Context Protocol (MCP) server, enabling streamlined access to comprehensive Indian consumer analytics data residing in a PostgreSQL database. It intelligently processes data by offering progressive context loading for schema and metadata, automatic weighting of user counts to accurately represent population segments, and simplified socioeconomic class merging (NCCS). With a robust RESTful HTTP API, this server facilitates easy integration, testing, and interaction with the rich dataset, making it ideal for applications requiring deep insights into Indian consumer behavior.

主要功能

01Progressive Context Loading (schema and metadata on demand)

020 GitHub stars

03Weighted Analysis for accurate population representation in user counts

04SQL Query Execution with automatic population weighting

05Automatic socioeconomic class (NCCS) merging

06RESTful HTTP API for seamless integration and testing

使用案例

01Integrating Indian consumer analytics data into AI models or applications (e.g., Claude Desktop)

02Analyzing mobile app usage patterns, user demographics, and socioeconomic trends in India

03Executing SQL queries on consumer analytics data with automatic weighting for population representation