Databases MCP Repositories

Interact with DuckDB databases, enabling data analysis and manipulation through the Model Context Protocol (MCP). Provides functions for querying and managing database interactions effectively.

A Model Context Protocol server for managing, monitoring, and querying data in [CockroachDB](https://cockroachlabs.com).

This Python-based utility establishes connectivity with the Trino distributed SQL query engine, often utilizing the Apache Iceberg table format for robust data organization. It provides standardized mechanisms for complex data querying, exploratory analysis, and essential table lifecycle management operations. A database, fundamentally an organized collection of data, relies on a DBMS for interaction; this tool acts as an application layer interacting with Trino, a powerful relational database access system.

Enables AI models to securely access and inspect MySQL database schemas while executing read-only SQL queries. Ensures data integrity through validation and read-only transactions, facilitating database exploration without modification risks.

Baserow database integration with table search, list, and row create, read, update, and delete capabilities.

Manage multiple JSON document databases with capabilities for creating, reading, updating, and deleting documents. Sync databases to the cloud for easy access and enable collaboration on structured data.

Server implementation for Google BigQuery integration that enables direct BigQuery database access and querying capabilities

Manage MySQL databases with functionality for executing read-only queries and safely testing write queries that are rolled back after execution.

Model Context Protocol (MCP) server that provides comprehensive SQLite database interaction capabilities.

Interact with MySQL databases through a standardized interface to perform operations such as querying, executing commands, and managing schemas securely. Provides comprehensive error handling and easy-to-use tools for database interactions.

Manage and search data in Redis using natural language queries. It enables interaction with Redis data structures like hashes, lists, sets, and streams for efficient data operations and insights.

Integrates with Neo4j graph database to perform graph database operations through natural language interactions.

Update databases by importing data from CSV and Excel files, with capabilities for managing documentation through built-in note-taking features. Supports PostgreSQL, MySQL, MongoDB, and SQLite for flexible database type management.

Handles SQL query execution, database management, API access, and user authentication with integrated safety controls.

Query and visualize OpenStreetMap data through a web-based interface, utilizing PostgreSQL/PostGIS for backend data management. Features include dynamic map interactions such as adding markers and polygons as well as adjustable view settings.

Run SQL queries and manage data within a PostgreSQL database. Connect AI for advanced data manipulation and analysis through simple command execution.

Integrate with Notion workspaces to manage content through LLMs, enabling functionality such as searching, creating, and updating pages and databases. Query and update database entries to streamline workflow and enhance productivity.

Connect to Elasticsearch data and interact with indices through natural language queries for intuitive data retrieval.

Connect and interact with MySQL databases to execute SQL queries and manage database connections. Analyze data through a structured interface tailored for AI model integration.

Execute SQL queries, manage CnosDB databases, and inspect table schemas through an MCP interface. Streamline database interactions with tools for listing databases and tables.

This service functions as a Model Context Protocol intermediary, facilitating structured read and write operations with Airtable environments. Airtable, fundamentally a cloud-based spreadsheet and database system, allows data organization into 'bases' and 'tables'. This tool enables large language models to query metadata, examine data structures, and manipulate records within these organized collections, similar to how a Database Management System (DBMS) manages core data interactions.

Connect to various database systems to execute queries, explore schemas, and manage data through a simple interface.

Provides read-only access to PostgreSQL databases, enabling the inspection of database schemas and the execution of read-only SQL queries.

This utility bridges Language Model applications with Microsoft SQL Server data stores using the Model Context Protocol (MCP). It provides secure, efficient mechanisms for LLMs to retrieve and modify structured information residing within these systems. A database management system (DBMS) facilitates this interaction, managing the organized collection of data, similar to how index cards once managed organizational records before digital systems became prevalent.

Manage Supabase databases, execute SQL queries, handle schema changes, and utilize the Supabase Management API and Auth Admin SDK with built-in safety controls.

Seamlessly interact with a Nocodb database to perform CRUD operations on its tables using the Model Context Protocol. Manage data efficiently with functionalities for adding, updating, deleting, and retrieving records.

Interact with Azure services to manage resources and perform operations, including support for Azure Blob Storage and Azure Cosmos DB (NoSQL API). Operations are logged and accessible through an audit resource endpoint.

Manage and retrieve knowledge efficiently using a vector database. Capable of intelligent storage and retrieval of various knowledge types, including best practices, lessons learned, and insights.

Leverages a caching solution to efficiently store and retrieve data, enabling quick access to frequently used values. Simplifies data management with straightforward commands for setting and getting cache values.

Leverage natural language to interact with Neo4j databases and manage knowledge graphs effortlessly. Transform natural language queries into Cypher commands and store knowledge graph memory in Neo4j or a file.