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Grocery Search MCP Server

Provides grocery price, search, and nutritional analysis through an MCP server for protein-per-dollar ranking and store comparisons.

Installation

Add the following to your MCP client configuration file.

Configuration

View docs

{
  "mcpServers": {
    "ajaykallepalli-mcp_food_search": {
      "command": "python",
      "args": [
        "-m",
        "grocery_search_mcp.server"
      ]
    }
  }
}

You can run and query the Grocery Search MCP Server to find grocery items, compare prices, and see nutritional information across supported stores. This server exposes a single, focused tool that powers AI agents to search for products, analyze nutrition, and rank protein value per dollar, all through the MCP interface.

How to use

To use the Grocery Prices search tool, connect your MCP client to the server and call the GroceryPrices.search endpoint. You can provide a product name to search for and optionally specify a store to narrow results. The tool will return a list of matching products with their price, package size, protein content, calories, and a protein-per-dollar ranking. Use cases include finding high-protein items for a specific budget, comparing prices across stores, and assessing overall nutritional value.

How to install

Prerequisites: ensure you have Python installed on your system.

Step 1: Clone the repository locally.

git clone <repository-url>
cd MCP_Food_Search

Step 2: Install dependencies.

pip install -r requirements.txt

Step 3: Install in development mode (optional but common during active development).

pip install -e .

Step 4: Start the MCP server using the standard Python invocation or the script entry point.

python -m grocery_search_mcp.server
```

```
grocery-search-mcp

Testing the implementation

Validate the server by running the provided test script to verify functionality.

python test_server.py

Available tools

GroceryPrices.search

Search for grocery items and obtain price, nutrition data, and protein-per-dollar calculations. Returns item details including name, brand, price, package size, protein, calories, and nutritional status.