This project provides a server based on the Model Context Protocol (MCP) for accessing the open data API of Statistics Netherlands (CBS), enabling AI tools to query statistical datasets, dimensions, and observations through the MCP protocol.

The Foundry MCP Server is a Model Context Protocol server for interacting with the Foundry platform, supporting AI assistants to operate on datasets, ontology objects, and execute functions.

The Honeycomb MCP server is an interface that enables Claude AI to interact with the Honeycomb API via the Model Context Protocol (MCP), allowing operations such as retrieving, creating, and updating datasets.

This project implements an API server that complies with the MCP protocol for searching the PRIDE Archive proteomics database and supports AI models to interact with proteomics datasets through structured function calls.

The Model Context Protocol (MCP) server developed for NASA Earth data search (CMR), which integrates AI retrieval with the NASA dataset catalog through Earthaccess.

MCP Hub is a framework for creating and managing Model Context Protocol (MCP) servers and clients, integrating the UV tool to simplify package management and configuration. The project includes AI - related computer vision scripts and dataset processing tools, supporting rapid deployment and development.

A model context protocol server for interacting with the Foundry platform, supporting AI assistants to operate on datasets, ontology objects, and execute functions.

The Honeycomb MCP Server is an interface service that enables Claude AI to interact with the Honeycomb API through the Model Context Protocol (MCP), implementing operations on datasets, queries, events, etc.

The Verodat MCP service is a protocol server for integrating code and data into AI systems (such as Claude Desktop), providing functions such as account management, dataset operations, and AI queries.

The 3GPP MCP Server V3.0.0 provides real-time data services for AI assistants to directly access 3GPP technical specifications through a lightweight API bridging architecture. It integrates the 535-million-word dataset of TSpec-LLM and the official 3GPP API to achieve sub-second structured specification queries.

Titanic AI Analysis is an educational project that demonstrates how to create a server through the MCP protocol to expose the Titanic dataset to language models such as Claude for analysis. The project includes data resources and tools, supporting complex queries and statistical analysis.