Databases MCP Repositories

Connects to Microsoft SQL Server databases and executes SQL queries, providing management tools for database connections.

Effortlessly connect applications to a serverless TiDB database, enabling management and querying of data through a streamlined interface. Focus on enhancing data-driven projects and simplifying database interactions.

Enable database interactions through the execution of SQL queries, the creation of tables, and exploration of schema information for efficient data management.

Automate and interact with InterSystems IRIS databases, streamlining data manipulation and retrieval processes. Enhance application development by leveraging the Model Context Protocol for efficient database interactions.

Universal MCP server tailored for databases, facilitating secure and compliant interactions optimized for large language models and AI agents. It supports a variety of database systems including PostgreSQL, MySQL, and ElasticSearch among others.

Distributed SQL database offering high availability and scalability with support for ACID transactions and strict consistency. Suitable for building interactive web services with automatic disaster recovery and horizontal scalability to accommodate varying workloads.

Automate schema learning, query validation, and execution for Gel databases using natural language interactions. Integrates with EdgeQL for efficient database management and facilitates seamless connections with LLMs to enhance user interactions.

MCP Forge generates new MCP servers from customizable templates, facilitating integration with Cursor IDE and deployment instructions through Smithery. Users can create servers with various functionalities, such as web search or database interactions.

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

Confluent integration to interact with Confluent Kafka and Confluent Cloud REST APIs.

Connect to Elasticsearch or OpenSearch clusters for natural language interaction, enabling operations such as searching documents, managing indices, and monitoring cluster health.

Enables LLMs to interact directly with CrateDB for optimized query handling and data insights, facilitating the ability to answer questions about data and assist with debugging and optimizing queries.

Postgres MCP Pro provides database health checks, index tuning, and safe SQL execution, facilitating robust software development. It offers tools like explain plans for query optimization and monitoring for overall database performance.

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

Enables function calls and message handling within the Lark ecosystem, utilizing a user's Lark account as an AI assistant. It automatically analyzes user input to invoke relevant functions and stores messages in a MySQL database for chat management.

Integrate SQL Server databases with AI assistants by facilitating secure and efficient database querying capabilities through the Model Context Protocol. Enable LLM applications to easily access and manipulate Microsoft SQL Server data.

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

Manage Elasticsearch clusters by executing queries, creating indices, and indexing documents. It facilitates data management and retrieval processes for large language models through seamless integration with Elasticsearch functionality.

Interact with Quickbase data using natural language, facilitating the management of apps, tables, and records. Leverages AI for efficient data access and manipulation through the Quickbase JSON RESTful API.

Provides capabilities for database interaction, allowing execution of SQL queries, data analysis, and automatic generation of business insight memos.

Interact with Astra DB to perform operations such as creating, updating, and deleting collections and records directly from Large Language Models. Enables seamless data interaction and enhances applications with database capabilities.

Integrates with Apache Gravitino services to manage metadata including catalogs, schemas, tables, tags, and user roles through an intuitive interface. Facilitates optimized metadata retrieval and management, enhancing data governance workflows.

Controls database access rights using database users, enabling seamless execution of SQL queries on Snowflake databases. Manages database connections and handles query results and errors securely, ensuring robust error handling and automatic connection management.

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.

Connects to SQL databases for executing queries, managing data, and accessing user management APIs with built-in safety controls.

friendly read-only mysql mcp server for cursor and n8n...

Connect and execute SQL queries on Snowflake databases while managing database connections and securely handling query results for AI applications.

Connect to over 20 different databases for data management tasks. Query, visualize, and edit data without writing SQL, utilizing AI-powered features for enhanced productivity.

Interact with dbt to execute CLI commands, query metrics from the semantic layer, and explore project models efficiently, streamlining data workflows.

A knowledge graph implementation that supports semantic search with a Qdrant vector database, enabling effective graph-based representation of entities and their relations. It includes features for file-based persistence and utilizes OpenAI embeddings for enhanced semantic similarity.