The Redis MCP Server is a natural language interface designed for agentic applications to efficiently manage and search data in Redis. It integrates seamlessly with MCP (Model Content Protocol) clients, enabling AI-driven workflows to interact with structured and unstructured data in Redis. 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"
- "Index and search this vector"
- 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.
This MCP Server provides tools to manage the data stored in Redis.
stringtools to set, get strings with expiration. Useful for storing simple configuration values, session data, or caching responses.hashtools 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.listtools with common operations to append and pop items. Useful for queues, message brokers, or maintaining a list of most recent actions.settools 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 settools to manage data for e.g. leaderboards, priority queues, or time-based analytics with score-based ordering.pub/subfunctionality to publish messages to channels and subscribe to receive them. Useful for real-time notifications, chat applications, or distributing updates to multiple clients.streamstools to add, read, and delete from data streams. Useful for event sourcing, activity feeds, or sensor data logging with consumer groups support.JSONtools 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.
query enginetools to manage vector indexes and perform vector searchserver managementtool to retrieve information about the database
Follow these instructions to install the server.
# Clone the repository
git clone https://github.com/aojah1/mcp_redis.git
cd mcp_redis
# Optional commands
How to actually get Python 3.13 on macOS (change it for your machine)
1 Homebrew (simplest)
bash
Edit
brew update
brew install python@3.13 # puts python3.13 in /opt/homebrew/bin
echo 'export PATH="/opt/homebrew/opt/python@3.13/bin:$PATH"' >> ~/.zshrc
exec $SHELL # reload shell so python3.13 is found
python3.13 --version # → Python 3.13.x
2 pyenv (lets you switch versions)
bash
Edit
brew install pyenv
pyenv install 3.13.0
pyenv global 3.13.0
python --version # now 3.13.0
# Install dependencies
# Install MCP Server requirement
python3.13 -m venv .venv
source .venv/bin/activate
python3.13 -m pip install -e .python3.13 -m venv .venv source .venv/bin/activate python3.13 -m pip install -r requirements.txt
To configure this Redis MCP Server, consider the following environment variables:
| Name | Description | Default Value |
|---|---|---|
REDIS_HOST |
Redis IP or hostname | "127.0.0.1" |
REDIS_PORT |
Redis port | 6379 |
REDIS_USERNAME |
Default database username | "default" |
REDIS_PWD |
Default database password | "" |
REDIS_SSL |
Enables or disables SSL/TLS | False |
REDIS_CA_PATH |
CA certificate for verifying server | None |
REDIS_SSL_KEYFILE |
Client's private key file for client authentication | None |
REDIS_SSL_CERTFILE |
Client's certificate file for client authentication | None |
REDIS_CERT_REQS |
Whether the client should verify the server's certificate | "required" |
REDIS_CA_CERTS |
Path to the trusted CA certificates file | None |
REDIS_CLUSTER_MODE |
Enable Redis Cluster mode | False |
Configure to use OCI GenAI Service as a next step: Used Cohere Model for ReAct
https://docs.oracle.com/en-us/iaas/Content/API/Concepts/sdkconfig.htm
Use OCI Resource Principle for production
And run the application.
python3.13 mcp_client/redis_langchain.py
To Test the Graph in Dev/Local run the following, you will get an IP/Port that can be used to connect from a lagraph client
langgraph dev --config langgraph.json --allow-blocking
python3.13 mcp_server/main.py The endpoint http://localhost:8000/sse?server=redis is reachable via curl. curl -N http://localhost:8000/sse?server=redis If it says "Not Found" or connection refused, the server isn’t running correctly.
** on another terminal run - python3.13 mcp_client/redis_langchain.py
There is an issue with the MCP Versions, both the server and the client has 2 different version. Here is the fix - In MCP_Client .env ==> use this ==> MCP_TRANSPORT=“streamable_http” In MCP_Server .env ==> use this ==> MCP_TRANSPORT=“streamable-http”
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:
docker build -t mcp_redis .
You can troubleshoot problems by tailing the log file.
tail -f ~/Library/Logs/mcp/mcp-server-redis.log
- 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.