Food MCP Server

Overview

The Food MCP Server is a Model Context Protocol (MCP) implementation that provides comprehensive food hierarchy and nutrition data through a structured API. Built with the MCP Python SDK and StreamableHTTP transport, the server delivers validated data responses using Pydantic schemas for enterprise-grade data consistency.

Architecture

The server architecture leverages the MCP Python SDK with StreamableHTTP transport to enable web-based access. All data responses are structured through Pydantic schemas, ensuring consistent validation and serialization across all endpoints.

Core Features

• Structured Data Output: All tool responses utilize Pydantic schemas for data validation and serialization

• StreamableHTTP Transport: Web-accessible MCP server implementation

• MongoDB Atlas Integration: Cloud-based data storage solution for scalability

• Container Support: Docker-based deployment with optimized build configurations

• Comprehensive Tool Suite: Eleven distinct tools covering food hierarchy and nutrition data

Available Tools

Food Hierarchy Management

1. get_all_food_hierarchy - Retrieve complete food hierarchy dataset

2. get_categories - List all available food categories

3. get_subcategories - Retrieve subcategories for a specified category

4. get_food_items - List food items within category or subcategory

5. search_food - Search food items using keyword parameters

6. find_food_category - Locate category for specific food item

7. list_all_foods - Retrieve all unique food names in dataset

8. food_stats - Generate comprehensive dataset statistics

Food Nutrition Analysis

9. list_food_names - List foods with available nutrition data

10. get_food_nutrition - Retrieve complete nutrition information for specified food

11. search_food_nutrition - Search nutrition data using keyword parameters

Data Structure Schemas

All tools return structured data using Pydantic validation schemas:

Food Hierarchy Schemas (schemas/food_hierarchy.py)

• FoodHierarchyResponse - Complete hierarchy data structure

• FoodCategoriesResponse - Category listing responses

• FoodSearchResponse - Search results with contextual information

• FoodStats - Comprehensive dataset statistics

Food Nutrition Schemas (schemas/food_item.py)

• FoodNutritionResponse - Complete nutrition data structure

• FoodNutrition - Detailed nutrition information with serving sizes

• ServingInfo - Structured serving size information

Installation and Setup

Local Development Environment

1. Environment Configuration:

   pip install -r requirements.txt cp .env.example .env # Configure your MONGODB_URI

2. Schema Validation Testing:

   python3 test_server.py

3. Server Initialization:

   python3 run_server.py # Server will be available at http://localhost:8000

4. MCP Inspector Integration:

   npx @modelcontextprotocol/inspector http://localhost:8000/mcp

5. Endpoint Access:

   • MCP StreamableHTTP endpoint: http://localhost:8000/mcp

   • CORS headers are enabled for browser access

   • Server logs provide startup information including listening address

Container Deployment

1. Image Build Process:

   docker build -t food-mcp-server .

2. Container Execution:

   docker run -e MONGODB_URI="your_mongodb_uri" -p 8000:8000 food-mcp-server

3. Docker Compose Deployment:

   docker-compose up --build

Configuration Management

Configure the following environment variables:

• MONGODB_URI - MongoDB Atlas connection string (required)

• PYTHONPATH - Set to /app in container environment

• PYTHONUNBUFFERED - Set to 1 for real-time logging output

Response Examples

Structured Categories Response

Structured Nutrition Response

Migration from FastMCP Implementation

This server implementation replaces the previous FastMCP implementation with several key advantages:

1. Enhanced Control: Low-level server implementation provides complete control over MCP protocol implementation

2. Structured Output: Pydantic schemas ensure consistent response format across all endpoints

3. Type Safety: Comprehensive type checking and validation throughout the application

4. Improved Debugging: Direct access to MCP internals for enhanced troubleshooting capabilities

5. Production Readiness: Architecture designed specifically for production deployment scenarios

Testing and Validation

Execute the comprehensive test suite to verify system functionality:

Test coverage includes:

• Pydantic schema validation and serialization

• JSON schema generation for tool definitions

• Structured data serialization processes

• Response format consistency verification

Development Guide

Project Structure

Adding New Tools

1. Define Pydantic schema in appropriate schemas/ module

2. Add tool definition in handle_list_tools() method

3. Implement tool handler in handle_call_tool() method

4. Return CallToolResult with structured content

5. Add corresponding tests in test_server.py

Production Deployment

The server is designed for production environments with the following features:

• Security: Non-root user execution in Docker container environment

• Logging: Structured logging implementation with appropriate severity levels

• Error Handling: Comprehensive error response mechanisms

• Health Monitoring: Docker health check configuration for container orchestration

• Resource Optimization: Multi-stage Docker builds for minimal image size

MCP Protocol Compliance

This server fully implements the Model Context Protocol specification:

• stdio Transport: Standard input/output communication protocol

• Tool Discovery: Dynamic tool listing with comprehensive schemas

• Structured Responses: Consistent response format across all endpoints

• Error Handling: Standardized error response structure

• Lifecycle Management: Proper initialization and cleanup procedures

Contributing Guidelines

1. Follow established Pydantic schema patterns for data validation

2. Add comprehensive test coverage for new features and functionality

3. Ensure Docker build processes complete successfully

4. Update documentation to reflect new tools and capabilities