mcp-redis: The official Redis MCP Server is a natural language interface designed for agentic applications to manage

by redis · MCP Server · ★ 641

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Featured in: MCP Database Tools

How to install mcp-redis

uvx --from git+https://github.com/redis/mcp-redis.git@0.2.0 redis-mcp-server --url redis://localhost:6379/0
uvx --from git+https://github.com/redis/mcp-redis.git redis-mcp-server --url redis://localhost:6379/0

Taken from the project's README on GitHub. Check the security audit before running it.

Security audit verdict: SAFE · quality 77/100

None of the 42 rules matched — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against 42 published rules built on the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →

The quality score combines nine heuristic checks of the repo and its README: completeness, clarity, specificity, examples, structure, agent readiness, step-by-step content, instruction quality and security notes. It rates how well the project explains itself to an agent, not the code.

databasegenaillmmcpmcp-serverredis

Quick Facts

Stars641
Forks123
LanguagePython
CategoryMCP Server
LicenseMIT
Quality Score77/100
Open Issues14
Last Updated2026-11-03
Created2025-04-02
Platformsmcp, python
Est. Tokens~20k

About mcp-redis

Redis MCP Server [![Stack Exchange questions](https://img.shields.io/stackexchange/stackoverflow/t/mcp-redis?style=social&logo=

Compatible Skills

These tools work well together with mcp-redis for enhanced workflows:

  • openchatbi — semantic(0.16)+complementary+rare_topics+same_lang+similar_pop+shared_platform (55%)
  • relevanceai — semantic(0.15)+complementary+same_lang+similar_pop+shared_platform (50%)
  • AgentChat — semantic(0.30)+rare_topics+same_lang+similar_pop+shared_platform (45%)

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How this security grade is produced

Grades come from a rule-based scan built on the SlowMist agent-security taxonomy: 42 published rules, covering credential harvesting, data exfiltration, curl | sh installers and more. It is a first-layer scan, not a manual audit — we say so rather than overstate it.

The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.

Sources & who's responsible:

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