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qualityA Model Context Protocol server that enables SQL query execution, database management, and business intelligence capabilities through MySQL connections.
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Why this server?
A Model Context Protocol server that enables SQL query execution, database management, and business intelligence capabilities through MySQL connections, suitable for statistical analysis on data.
Why this server?
Runs a language server and provides tools for communicating with it. Language servers excel at tasks that LLMs often struggle with, such as precisely understanding types, understanding relationships, and providing accurate symbol references which is useful in statistical data analysis.
Why this server?
Full featured MCP Server for MongoDB database analysis, this server is helpful in performing statistics on data stored in MongoDB.
Why this server?
A Model Context Protocol server that provides database interaction capabilities through SQLite, enabling users to run SQL queries, analyze business data, and automatically generate business insight memos which is useful in computing different types of statistics.
Why this server?
MCP Server for interacting with Cube semantic layers that provides tools for querying and describing data from Cube deployments, which can be used to perform statistical analysis on snowflake data.
Why this server?
Provides RAG capabilities for semantic document search using Qdrant vector database and Ollama/OpenAI embeddings, allowing users to add, search, list, and delete documentation with metadata support for advanced statistical analysis.
Why this server?
Provides an MCP protocol interface for interacting with Elasticsearch 7.x databases, supporting comprehensive search functionality including aggregations, highlighting, and sorting for conducting advanced statitics.
Why this server?
This repository is an example of how to create a MCP server for Qdrant, a vector search engine that allows for clustering the data.
Why this server?
A high-performance FastAPI server supporting Model Context Protocol (MCP) for seamless integration with Large Language Models, featuring REST, GraphQL, and WebSocket APIs, along with real-time monitoring and vector search capabilities which is helpful in clustering.