"Information about food" matching MCP servers:

An MCP Server implementation that integrates the Balldontlie API, to provide information about players, teams and games for the NBA, NFL and MLB.

Enables access to the Hugging Face Hub API to search and retrieve information about machine learning models, datasets, and their metadata. Provides comprehensive tools for exploring the Hugging Face ecosystem including model details, dataset information, and parquet file access.

Enables local drink inventory management, cocktail recipe retrieval, and food pairing recommendations through MCP.

A basic implementation of persistent memory using a local knowledge graph. This lets Claude remember information about the user across chats.

Enables large language models to retrieve up-to-date and correct information about dlt pipelines, datasets, and schemas, improving AI-assisted development.

A Model Context Protocol server that provides natural language access to Cortex API, enabling users to query information about their system's structure and make faster decisions about services, incidents, and team responsibilities.

Enables users to perform seeded food-order, delivery, refund, and invoice operations through Model Context Protocol tools.

An MCP server that provides detailed information about your development environment to the Cursor code editor, enabling more context-aware assistance.

Provides access to USDA FoodData Central database, enabling food search, nutrition details, and comparison for dietary analysis via MCP.

A Python-based server that interacts with EPICS process variables, allowing users to retrieve PV values, set PV values, and fetch detailed information about PVs through a standardized interface.

An MCP server that provides standardized access to Pokemon data, allowing users to search, compare, and retrieve detailed information about Pokemon through natural language tools.

Enables AI-powered natural language queries about BIT Information Portal (BIP) data by integrating with BIP APIs through session management. Uses Google Gemini to intelligently route questions to appropriate endpoints and generate contextual answers about students, departments, courses, faculty, and achievements.

Enables tracking food intake and nutrition using the USDA FoodData Central database. Supports logging meals, setting daily nutrition goals, viewing food diaries, and analyzing nutrition trends over time with local SQLite storage.

Enables querying a Neo4j knowledge graph about Wroclaw University of Science and Technology using natural language. Converts user questions into Cypher queries and retrieves contextual information through an intelligent RAG pipeline with LLM-powered query routing.

Provides comprehensive food hierarchy and nutrition data through structured tools that enable searching foods, browsing categories, and retrieving detailed nutritional information from a MongoDB Atlas database.

Enables multi-agent trip planning with coordinated specialized agents for team management, food preferences, and travel recommendations. Integrates Neo4j employee graphs, Postgres data, real-time weather APIs, and country information for comprehensive travel planning.

Retrieves comprehensive patient information including personal details, insurance plans, and employee data through the get_patient tool. Supports Docker Compose deployment and integration with Watson Orchestrate for healthcare data access.

A server that allows AI agents to query and retrieve information about upcoming events at Berghain nightclub through a DynamoDB-backed FastAPI service.

Enables searching and retrieving detailed information about Swedish companies, including financial data and annual reports from Bolagsverket (Swedish Companies Registration Office), with intelligent caching for fast responses.

MCP server that allows AI assistants to query and retrieve information about office supplies inventory from a CSV file.

Enables AI chatbots to look up standardized Korean food composition data, retrieve nutrient values, and check or grade nutrition answers against the National Standard Food Composition DB 10.4 with source attribution.

An MCP server that provides a USDA-accurate food database with deterministic macro math, enabling tools like list_foods, get_food, calculate_macros, filter_by_diet, build_meal, and list_available_tags. It allows Claude to look up nutrition facts and solve for exact macro targets offline, without AI or network.

An MCP server that gives models semantic context about a data warehouse, including table meaning, units, lineage, and health, to reduce guessing.

Enables AI agents to answer analytical questions about an online store's SQLite database through specialized read-only tools, without any risk of modifying the underlying data.

Provides SQL access to the ANSES Ciqual French food composition database, enabling query of nutritional data for over 3,000 foods with full-text search support.

Enables Claude to run read-only SQL queries against the DWH.facturas table in SQL Server, including discovering its columns and asking natural-language questions about invoice data.

Lets AI assistants like Cursor and Claude safely explore your database and answer questions about your data in plain English.

Enables users to answer California drinking water questions about public water systems, SAFER risk/violation status, source-water chemistry, school lead sampling, and latest tap/treated-water results by ZIP code or water system ID, using curated Databricks Gold views over State Water Board open data.

Provides SQL access to the ANSES Ciqual French food composition database with nutritional data for over 3,000 foods. Supports full-text search and bilingual queries for comprehensive nutrition analysis.

MCP server to ask research questions about real-world datasets and get actual database records back, with auto-registration and trial credit.