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Give LLM agents structured local-first access to e-commerce data.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "Project Tango" yet — see the docs or source repo.
Using Project Tango, read product and inventory data, list the 10 SKUs with the lowest stock, rank them by stockout risk, and return a table.
A table showing SKU, product name, current inventory, and risk ranking.
Use Project Tango order and sales analytics data to summarize order volume, revenue, and key trends over a recent period, and identify the best-performing product category.
A sales trend summary with key metrics organized by category.
Using Project Tango's mock e-commerce dataset, demonstrate how an agent can query products, check inventory, and then join order information to generate a briefing.
A sample brief showing multi-step data retrieval and combined results.
Developers can test agent data access and reasoning flows without connecting to real business systems. This helps validate MCP-based tool usage faster with a local-first mock dataset.
Data analysts or product managers can have an LLM read product, order, inventory, and sales analytics data to generate quick prototype insights. It is useful for validating analysis needs in a zero-configuration setup.
After validating a prototype, developers can connect live APIs through the swappable DataProvider interface. This enables a transition from mock data to production data sources while keeping a similar access pattern.
It is a local-first MCP server that gives LLM agents structured access to e-commerce data. The available data includes products, inventory, orders, and sales analytics.
The description says it offers zero configuration, so it should be easy to start with. For exact installation and runtime steps, see the source repository.
Yes. The description mentions a swappable DataProvider interface for moving to live APIs. For supported integrations and implementation details, see the source repository.
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