Redis MCP Server

The Redis MCP Server is Redis Ltd.'s official open-source server for the Model Context Protocol. According to the official announcement blog post and Redis documentation at redis.io/docs/latest/integrate/redis-mcp/, it connects an MCP-capable AI client to a specific Redis instance, turning "an LLM client into a very high-level interface to Redis," as Redis puts it. The repository lives at github.com/redis/mcp-redis, is MIT licensed, and is maintained directly by Redis itself rather than an independent community project. For Skill Road, this is a distinct entry because Redis, as an in-memory data structure store, follows different working patterns than classic SQL, document, or analytics databases such as MongoDB, ClickHouse, or Snowflake.

Data structures and tools

According to the README, the server covers practically the full Redis command surface: string, hash, list, set, and sorted-set operations, JSON document management, pub/sub channels for real-time communication, streams with consumer-group support, and vector search with index management for retrieval-augmented generation. It also includes server-management tools for health-checking the connected instance and a documentation-search function. Typical tasks include session management, conversation history, real-time caching, rate limiting, recommendation logic, or semantic search for RAG applications — the exact use cases Redis itself highlights. Data Analysis and Coding are the fitting categories because the server supports both data exploration and development work around caching and session logic.

Installation and deployment modes

The recommended path goes through PyPI: uvx --from redis-mcp-server@latest redis-mcp-server --url "redis://localhost:6379/0" starts the server locally over stdio and connects it to a Redis instance through a connection URI. The repository also documents installation directly from source via uv sync and an official Docker image published as mcp/redis on Docker Hub. Redis does not offer a self-hosted public MCP remote endpoint for this project; the server runs locally or in a self-controlled environment and connects from there to a local Redis instance, a self-managed server, or Redis Cloud. For managing a Redis Cloud account itself — creating new databases or checking subscriptions, for example — Redis provides the separate mcp-redis-cloud project, which is not part of this entry.

Configuration, ACLs, and encryption

The connection is configured through environment variables such as REDIS_HOST, REDIS_PORT, REDIS_DB, REDIS_PWD, REDIS_USERNAME, and REDIS_SSL; cluster operation has its own REDIS_CLUSTER_MODE, and Azure Managed Redis gets dedicated EntraID authentication with automated token refresh. The README recommends SSL/TLS encryption for the connection and the use of Redis ACLs so an agent only receives the commands and key spaces it actually needs, rather than administrative access. Credentials belong in environment variables or the MCP client's secure configuration, not in prompts, repositories, or screenshots. Because Redis commands can both read and write or delete data, a first setup should start with a non-production instance, a narrowly scoped ACL role, and a clearly bounded task.

Data path, prompt injection, and fit

The server's tool calls reach the configured Redis instance and return results to the connected MCP client; what that client subsequently does with the data — placing it into model context, logging it, or forwarding it to an external model provider — depends on the chosen client and its settings, not on the MCP server itself. Values retrieved from Redis, for example in streams, lists, or hashes, should generally be treated as data rather than a trusted instruction, since they could in principle carry a prompt injection. The repository is MIT licensed; the GitHub API reported exactly 623 stars immediately before this entry was created, at current release v0.5.1. That figure is a point-in-time popularity snapshot, not a security or quality guarantee. The server itself is free and open source, while costs for the Redis instance it connects to depend on self-hosting, infrastructure, or a chosen Redis Cloud tier; this public entry therefore does not state fixed prices.

Who benefits from the Redis MCP Server?

The server is particularly useful for teams that already run Redis as a cache, session store, message broker, or vector index and want an AI agent to query or maintain those data structures in natural language — for example while debugging caching issues, tracing queue state, or building a RAG pipeline that uses Redis as a vector store. It is less suited as a standalone tool for complex relational analysis; the catalog's existing SQL and data-warehouse servers fit that need better.

Python 3.10 or newer, uv/uvx or Docker, an MCP-capable client, and a reachable Redis instance with suitable credentials.

Configure uvx --from redis-mcp-server@latest redis-mcp-server --url "redis://localhost:6379/0" in the MCP client, or use the official mcp/redis Docker image. Secure the connection with SSL/TLS and use a Redis user with a narrowly scoped ACL instead of administrative rights.

uvx --from redis-mcp-server@latest redis-mcp-server

Connection via Redis host, port, username/password, or connection URI; Azure Managed Redis additionally supports EntraID OAuth with automatic token refresh.

Permissions follow the Redis ACL or Redis user in use; Redis recommends narrowly scoped ACL rules instead of full access.

Tool calls read and write data in the configured Redis instance; results return to the MCP client and can enter model context or logging there.

Overly broad ACL rights, unencrypted connections, credentials in prompts, or write/delete access to production instances can expose or alter data.