MCPs tagged with # data analytics

Provides tools to deliver real-time Bitcoin blockchain data for use with Model Context Protocol (MCP) clients. Offers specialized endpoints for querying blockchain addresses, transactions, UTXOs, and block information by interfacing with the mempool.space API. Supports easy installation and integration with popular MCP clients, enabling seamless access to blockchain analytics.

• ⭐ 0
• MCP
• JamesANZ/bitcoin-mcp

Zaturn enables AI models to interact with multiple data sources and generate analytics without requiring users to write SQL or Python code. It supports SQL databases and file formats, providing visual and tabular summaries and offering an interactive web interface similar to Jupyter Notebook. Zaturn can function both as a standalone platform or as a Model Context Protocol (MCP) compliant tool, allowing seamless context management for AI-driven data analysis.

• ⭐ 66
• MCP
• kdqed/zaturn

Databricks Genie MCP Server enables interaction between large language models and the Databricks Genie API using the Model Context Protocol. It allows users to ask natural language questions, start and manage conversations, and run SQL queries in Genie spaces. The tool provides structured results, supports follow-up queries, and facilitates connection through both standard and Docker-based setups. Designed for use with Claude Desktop, it streamlines conversational analytics within Databricks workspaces.

• ⭐ 12
• MCP
• yashshingvi/databricks-genie-MCP

MCP Tinybird enabled analytics data ingestion from Tinybird to systems following the Model Context Protocol (MCP). Now deprecated, it guided users to migrate towards the new official Tinybird MCP Server implementation for maintaining up-to-date MCP support.

• ⭐ 74
• MCP
• tinybirdco/mcp-tinybird

AWS MCP Servers is a suite of specialized servers implementing the open Model Context Protocol (MCP) to bridge large language model (LLM) applications with AWS services, tools, and data sources. It provides a standardized way for AI assistants, IDEs, and developer tools to access up-to-date AWS documentation, perform cloud operations, and automate workflows with context-aware intelligence. Featuring a broad catalog of domain-specific servers, quick installation for popular platforms, and both local and remote deployment options, it enhances cloud-native development, infrastructure management, and workflow automation for AI-driven tools. The project includes Docker, Lambda, and direct integration instructions for environments such as Amazon Q CLI, Cursor, Windsurf, Kiro, and VS Code.

• ⭐ 6,220
• MCP
• awslabs/mcp