CustomRedis MCP Server

Overview

The CustomRedis MCP server bridges a standard Redis instance with the Model Control Protocol, allowing AI assistants such as Claude to perform key-value operations directly through MCP calls. By exposing a lightweight, container‑ready service that bundles both Redis and the MCP interface, it removes the need for developers to write custom adapters or manage separate networking setups. The server offers persistent storage, comprehensive error handling, and configurable logging—all within a single Docker image that can be spun up with .

This solution solves the common pain point of integrating in‑memory caches or stateful data stores into conversational AI workflows. Rather than building bespoke REST endpoints or using generic database connectors, developers can invoke Redis commands as first‑class MCP tools. This guarantees consistent request/response contracts, built‑in retry logic, and the ability to audit operations through the server’s logging pipeline. The result is a declarative way to manage session data, feature flags, or transient computations directly from an AI assistant.

Key capabilities include:

• CRUD operations via , , and for straightforward key-value manipulation.
• Pattern‑based enumeration with , enabling AI agents to discover related data without hard‑coded key lists.
• Health checks that expose Redis service status, ensuring the assistant can fail gracefully if connectivity drops.
• Environment‑driven configuration (host, port, database number, log level) that aligns with modern container orchestration practices.
• Robust error handling covering connection failures, command errors, and graceful shutdowns, which protects the AI workflow from unexpected crashes.

Typical use cases include:

• Session persistence: Storing user preferences or conversation context that an AI assistant can retrieve in subsequent turns.
• Feature gating: Toggling experimental capabilities on or off by setting flags in Redis and querying them via MCP.
• Rate limiting: Tracking request counts per user or API key to enforce limits within the AI’s control logic.
• Cache orchestration: Caching expensive computations or external API responses that the assistant can reuse without re‑invoking costly services.

Integration is seamless with existing MCP‑enabled assistants. By adding a single entry to the configuration in Claude Desktop, developers can launch the server on demand. The Docker command ensures the MCP process shares a network namespace with the Redis container, guaranteeing low‑latency communication. Once configured, the assistant can call any of the exposed Redis operations as if they were native tools, benefiting from consistent authentication, logging, and error reporting provided by the MCP framework.