Redis MCP Server

• Overview
• Features
• Tools
• Installation
  • From PyPI (recommended)
  • Testing the PyPI package
  • From GitHub
  • Development Installation
  • With Docker
• Configuration
  • Redis ACL
  • Configuration via command line arguments
  • Configuration via Environment Variables
  • EntraID Authentication for Azure Managed Redis
  • Logging
• Integrations
  • OpenAI Agents SDK
  • Augment
  • Claude Desktop
  • VS Code with GitHub Copilot
• Testing
• Example Use Cases
• Contributing
• License
• Badges
• Contact

Features

• Natural Language Queries: Enables AI agents to query and update Redis using natural language.
• Seamless MCP Integration: Works with any MCP client for smooth communication.
• Full Redis Support: Handles hashes, lists, sets, sorted sets, streams, and more.
• Search & Filtering: Supports efficient data retrieval and searching in Redis.
• Scalable & Lightweight: Designed for high-performance data operations.
• EntraID Authentication: Native support for Azure Active Directory authentication with Azure Managed Redis.
• The Redis MCP Server supports the stdio transport. Support to the stremable-http transport will be added in the future.

Tools

This MCP Server provides tools to manage the data stored in Redis.

• string tools to set, get strings with expiration. Useful for storing simple configuration values, session data, or caching responses.
• hash tools to store field-value pairs within a single key. The hash can store vector embeddings. Useful for representing objects with multiple attributes, user profiles, or product information where fields can be accessed individually.
• list tools with common operations to append and pop items. Useful for queues, message brokers, or maintaining a list of most recent actions.
• set tools to add, remove and list set members. Useful for tracking unique values like user IDs or tags, and for performing set operations like intersection.
• sorted set tools to manage data for e.g. leaderboards, priority queues, or time-based analytics with score-based ordering.
• pub/sub functionality to publish messages, create stateful channel or pattern subscriptions, and read queued messages using returned subscription handles. Useful for real-time notifications, chat applications, or distributing updates to multiple clients.
• streams tools to add, read, delete, create and destroy consumer groups, and acknowledge processed entries in data streams. Useful for event sourcing, activity feeds, and worker-based event processing with Redis Streams consumer groups.
• JSON tools to store, retrieve, and manipulate JSON documents in Redis. Useful for complex nested data structures, document databases, or configuration management with path-based access.

Additional tools.

• docs tool to search Redis documentation, tutorials, and best practices using natural language questions (backed by the MCP_DOCS_SEARCH_URL HTTP API).
• query engine tools to manage vector indexes and perform vector search
• server management tool to retrieve information about the database

Installation

The Redis MCP Server is available as a PyPI package and as direct installation from the GitHub repository.

From PyPI (recommended)

Configuring the latest Redis MCP Server version from PyPI, as an example, can be done importing the following JSON configuration in the desired framework or tool.
The uvx command will download the server on the fly (if not cached already), create a temporary environment, and then run it.

URL specification

The format to specify the --url argument follows the redis and rediss schemes:

As an example, you can easily connect to a localhost server with:

Where 0 is the logical database you'd like to connect to.

For an encrypted connection to the database (e.g., connecting to a Redis Cloud database), you'd use the rediss scheme.

To verify the server's identity, specify ssl_ca_certs.

For an unverified connection, set ssl_cert_reqs to none

Configure your connection using the available options in the section "Available CLI Options".

Testing the PyPI package

You can install the package as follows:

And start it using uv the package in your environment.

However, starting the MCP Server is most useful when delegate to the framework or tool where this MCP Server is configured.

From GitHub

You can configure the desired Redis MCP Server version with uvx, which allows you to run it directly from GitHub (from a branch, or use a tagged release).

It is recommended to use a tagged release, the main branch is under active development and may contain breaking changes.

As an example, you can execute the following command to run the 0.2.0 release:

Check the release notes for the latest version in the Releases section.
Additional examples are provided below.

Development Installation

For development or if you prefer to clone the repository:

Once you cloned the repository, installed the dependencies and verified you can run the server, you can configure Claude Desktop or any other MCP Client to use this MCP Server running the main file directly (it uses environment variables). This is usually preferred for development.
The following example is for Claude Desktop, but the same applies to any other MCP Client.

1. Specify your Redis credentials and TLS configuration
2. Retrieve your uv command full path (e.g. which uv)
3. Edit the claude_desktop_config.json configuration file
   • on a MacOS, at ~/Library/Application\ Support/Claude/

You can troubleshoot problems by tailing the log file.

With Docker

You can use a dockerized deployment of this server. You can either build your own image or use the official Redis MCP Docker image.

If you'd like to build your own image, the Redis MCP Server provides a Dockerfile. Build this server's image with:

Finally, configure the client to create the container at start-up. An example for Claude Desktop is provided below. Edit the claude_desktop_config.json and add:

To use the official Redis MCP Docker image, just replace your image name (mcp-redis in the example above) with mcp/redis.

Configuration

The Redis MCP Server can be configured in two ways: via command line arguments or via environment variables.
The precedence is: command line arguments > environment variables > default values.

Redis ACL

You can configure Redis ACL to restrict the access to the Redis database. For example, to create a read-only user:

Configure the user via command line arguments or environment variables.

Configuration via command line arguments

When using the CLI interface, you can configure the server with command line arguments:

Available CLI Options:

• --url - Redis connection URI (redis://user:pass@host:port/db)
• --host - Redis hostname (default: 127.0.0.1)
• --port - Redis port (default: 6379)
• --db - Redis database number (default: 0)
• --username - Redis username
• --password - Redis password
• --ssl - Enable SSL connection
• --ssl-ca-path - Path to CA certificate file
• --ssl-keyfile - Path to SSL key file
• --ssl-certfile - Path to SSL certificate file
• --ssl-cert-reqs - SSL certificate requirements (default: required)
• --ssl-ca-certs - Path to CA certificates file
• --cluster-mode - Enable Redis cluster mode

Configuration via Environment Variables

If desired, you can use environment variables. Defaults are provided for all variables.

EntraID Authentication for Azure Managed Redis

The Redis MCP Server supports EntraID (Azure Active Directory) authentication for Azure Managed Redis, enabling OAuth-based authentication with automatic token management.

Authentication Providers

Service Principal Authentication - Application-based authentication using client credentials:

Managed Identity Authentication - For Azure-hosted applications:

Default Azure Credential - Automatic credential discovery (recommended for development):

EntraID Configuration Variables

Key Features

• Automatic token renewal - Background token refresh with no manual intervention
• Graceful fallback - Falls back to standard Redis authentication when EntraID not configured
• Multiple auth flows - Supports Service Principal, Managed Identity, and Default Azure Credential
• Enterprise ready - Designed for Azure Managed Redis with centralized identity management

Example Configuration

For local development with Azure CLI:

For production with Service Principal:

For Azure-hosted applications with Managed Identity:

There are several ways to set environment variables:

1. Using a .env File:
   Place a .env file in your project directory with key-value pairs for each environment variable. Tools like python-dotenv, pipenv, and uv can automatically load these variables when running your application. This is a convenient and secure way to manage configuration, as it keeps sensitive data out of your shell history and version control (if .env is in .gitignore).
   For example, create a .env file with the following content from the .env.example file provided in the repository:

Then edit the .env file to set your Redis configuration:

OR,

2. Setting Variables in the Shell:
   You can export environment variables directly in your shell before running your application. For example:

This method is useful for temporary overrides or quick testing.

Logging

The server uses Python's standard logging and is configured at startup. By default it logs at WARNING and above. You can change verbosity with the MCP_REDIS_LOG_LEVEL environment variable.

• Accepted values (case-insensitive): DEBUG, INFO, WARNING, ERROR, CRITICAL, NOTSET
• Aliases supported: WARN → WARNING, FATAL → CRITICAL
• Numeric values are also accepted, including signed (e.g., "10", "+20")
• Default when unset or unrecognized: WARNING

Handler behavior

• If the host (e.g., uv, VS Code, pytest) already installed console handlers, the server will NOT add its own; it only lowers overly-restrictive handler thresholds so your chosen level is not filtered out. It will never raise a handler's threshold.
• If no handlers are present, the server adds a single stderr StreamHandler with a simple format.

Examples

In MCP client configs that support env, add it alongside your Redis settings. For example:

Integrations

Integrating this MCP Server to development frameworks like OpenAI Agents SDK, or with tools like Claude Desktop, VS Code, or Augment is described in the following sections.

OpenAI Agents SDK

Integrate this MCP Server with the OpenAI Agents SDK. Read the documents to learn more about the integration of the SDK with MCP.

Install the Python SDK.

Configure the OpenAI token:

And run the application.

You can troubleshoot your agent workflows using the OpenAI dashboard.

Augment

The preferred way of configuring the Redis MCP Server in Augment is to use the Easy MCP feature.

You can also configure the Redis MCP Server in Augment manually by importing the server via JSON:

Claude Desktop

The simplest way to configure MCP clients is using uvx. Add the following JSON to your claude_desktop_config.json, remember to provide the full path to uvx.

Basic Redis connection:

Azure Managed Redis with EntraID authentication:

VS Code with GitHub Copilot

To use the Redis MCP Server with VS Code, you must nable the agent mode tools. Add the following to your settings.json:

You can start the GitHub desired version of the Redis MCP server using uvx by adding the following JSON to your mcp.json file:

Suppressing uvx Installation Messages

If you want to suppress uvx installation messages that may appear as warnings in MCP client logs, use the -qq flag:

The -qq flag enables silent mode, which suppresses "Installed X packages" messages that uvx writes to stderr during package installation.

Alternatively, you can start the server using uv and configure your mcp.json. This is usually desired for development.

For more information, see the VS Code documentation.

Tip: You can prompt Copilot chat to use the Redis MCP tools by including #redis in your message.

Note: Starting with VS Code v1.102,
MCP servers are now stored in a dedicated mcp.json file instead of settings.json.

Testing

You can use the MCP Inspector for visual debugging of this MCP Server.

Example Use Cases

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

Contributing

1. Fork the repo
2. Create a new branch (feature-branch)
3. Commit your changes
4. Push to your branch and submit a PR!

License

This project is licensed under the MIT License.

Badges

Contact

For questions or support, reach out via GitHub Issues.

Alternatively, you can join the Redis Discord server and ask in the #redis-mcp-server channel.