Azure Managed Redis MCP Server provides a natural language interface for agentic apps to interact with Azure Managed Redis—a high-speed, in-memory datastore that is ideal for low-latency use cases like agent memory, vector data store and semantic caching.

This repo is a fork of mcp-redis with following updates:

• Support SSE so that this MCP server can be hosted remotely
• Support server side authentication through API Keys or OAuth
• Uses Entra Id Authentication by default to connect to Azure Managed Redis instance
• Removed the use of 'anyOf' keyword which cannot be parsed by some older MCP clients
• Allows bringing your own vectorizer for vector search use cases with the "knowledge store" tools
• Azd support to host MCP server remotely on Azure Container Apps

Using this MCP Server, you can ask questions like:

• "Store the entire conversation in a stream"
• "Cache this item"
• "Store the session with an expiration time"
• "Store this json data as a vector"
• "Index and search this vector"

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 to channels and subscribe to receive them. Useful for real-time notifications, chat applications, or distributing updates to multiple clients.
• streams tools to add, read, and delete from data streams. Useful for event sourcing, activity feeds, or sensor data logging with consumer groups support.
• 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.
• knowledge store tools to store and retrieve data using semantic similarity search with vector embeddings powered by Azure OpenAI. Useful for building RAG systems, semantic caching, question-answering systems, or any application that needs to find relevant information based on meaning rather than exact matches.

Additional tools.

• query engine tools to manage vector indexes and perform vector search
• server management tool to retrieve information about the database

Run the Redis MCP Server locally

The fastest way to deploy the MCP Server to Azure is using Azure Developer CLI (azd):

# Install azd (if not already installed)
curl -fsSL https://aka.ms/install-azd.sh | bash

# Login and deploy
azd auth login
azd up

This single command will:

• ✅ Prompt for environment name, subscription, and location
• ✅ Deploy Azure Managed Redis with RediSearch and RedisJSON
• ✅ Deploy Container Apps with the MCP server
• ✅ Build and push the container image
• ✅ Configure authentication (NO-AUTH, API-KEY, or OAUTH)

See detailed instructions here

Alternatively, you can use the interactive deployment script:

./infra/deploy-redis-mcp.sh

This script provides full control over the deployment configuration including resource group, location, Redis SKU, and authentication method.

For detailed deployment options, see docs/AZURE_DEPLOYMENT.md.

Configure a client list VSCode GitHub Copilot to create MCP client for testing. Edit the mcp.json and add:

{
  "servers": {
    "redis": {
      "type": "http",
      "url": "https://<your-Redis-MCP-server-url>/message",
      "headers": {
        "X-API-Key": "<your-api-key>"
      }
    }
  }
}