redis/mcp-redis

Category: database-messaging-mcp-servers
Tags: mcp, redis, database, open-source
Source: https://github.com/redis/mcp-redis

Description

The official Redis MCP Server is a natural language interface designed for agentic applications to manage and search data in Redis efficiently. It integrates with MCP (Model Content Protocol) clients, enabling AI-driven workflows to interact with both structured and unstructured data in Redis using natural language commands.

Features

• Natural Language Queries: Allows AI agents or users to query and update Redis using natural language.
• Seamless MCP Integration: Works with any MCP client for smooth communication.
• Full Redis Data Type Support: Handles operations on strings, hashes (including vector embeddings), lists, sets, sorted sets, streams, and JSON documents.
• Search & Filtering: Supports efficient data retrieval and search operations within Redis.
• Scalable & Lightweight: Designed for high-performance data operations.
• Pub/Sub Functionality: Supports publish/subscribe messaging for real-time notifications and updates.
• Stream Tools: Add, read, and delete from data streams, with support for consumer groups.
• Vector Index Management: Query engine tools to manage vector indexes and perform vector searches.
• Server Management Tools: Retrieve information about the Redis database instance.
• JSON Tools: Store, retrieve, and manipulate JSON documents with path-based access.
• Flexible Configuration: Environment variables allow configuration of Redis host, port, authentication, SSL/TLS, and cluster mode.
• Integration with AI Platforms: Works with OpenAI Agents SDK and Claude Desktop (via Smithery or manual configuration).
• Docker Support: Includes a Dockerfile for containerized deployment.
• Visual Debugging: Use MCP Inspector for visual debugging and inspection.

Example Use Cases

• AI Assistants: Enable LLMs to fetch, store, and process data in Redis.
• Chatbots & Virtual Agents: Retrieve session data, manage queues, personalize responses.
• Data Search & Analytics: Query Redis for real-time insights and lookups.
• Event Processing: Manage event streams with Redis Streams.

Installation & Deployment

• Manual Installation: Clone the repository, set up a Python environment, and install dependencies with uv.
• Docker: Build and run the server as a Docker container.
• Integration with AI SDKs: Works with OpenAI Agents SDK and Claude Desktop.

Configuration

Configurable via environment variables for Redis host, port, username, password, SSL/TLS, and cluster mode.

License

MIT License

Pricing

No pricing information provided; the project is open-source under the MIT License.