MCP server
Redis MCP server
Redis's official MCP server lets AI read and write Redis data structures, search vector indexes and inspect server information.
- Official server
- Databases
At a glance
- Maintained by
- Redis
- Tools exposed
- 10 listed below
- Reviewed
- September 24, 2026
Why it matters
What it does and why it matters
Redis is an in-memory data store used for caching, sessions, queues, rate limits and, more and more, vector search. The official Redis MCP server lets an AI assistant work with Redis data in plain English: get and set values, read hashes, scan keys, search vector indexes and check server information.
For developers, it is a quick way to inspect what is in a cache or queue while debugging. For AI features, it can act as a fast store for memory or search when combined with vector indexes.
It has read and write access across data types, and we found no read-only mode documented. Redis often holds sessions and other sensitive runtime data, so connect it only to development or dedicated instances, with a user whose access control list allows only the commands and keys a task needs.
Use cases
Best business use cases
Debugging caches
Queue checks
Vector search
Agent memory
Server health
Tools
Available tools
Tool names as published by the maintainer at review time. Versions change, so check the repository for the current list.
Read and write: Read and write across strings, hashes, lists, sets, streams, JSON and vector indexes; no read-only mode documented.
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setSets a string value with optional expiry.
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getGets a string value.
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hsetSets a field in a hash.
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hgetallGets all fields and values in a hash.
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scan_keysScans keys with the non-blocking SCAN command.
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deleteDeletes a key.
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vector_search_hashRuns a KNN vector search over vectors stored in hashes.
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get_indexesLists search indexes.
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infoReturns Redis server information and stats.
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search_redis_documentsSearches Redis documentation and knowledge base.
Workflows
Example workflows
Debug a stale cache
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1
Connect to the development instance.
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2
Ask the assistant to find keys for a product.
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3
It scans and reads the cached values.
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4
It compares them with the database record.
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5
The developer clears the stale key after review.
Check vector search results
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1
Ask which vector indexes exist.
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2
Run a sample search through the assistant.
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3
Review the returned documents.
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4
Adjust the query or index settings.
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5
Repeat with real user questions.
Prompts
Example prompts
“Find cache keys for product 1042 and show their values.”
“Which vector indexes exist, and how many documents does each hold?”
“Show memory usage and the number of connected clients.”
Setup
Installation and configuration
Examples for Claude Desktop and Claude Code. Replace the placeholder values with your own keys, and store secrets outside version control.
Claude Desktop: claude_desktop_config.json
{
"mcpServers": {
"redis-mcp-server": {
"type": "stdio",
"command": "/path/to/uvx",
"args": [
"--from",
"redis-mcp-server@latest",
"redis-mcp-server",
"--url",
"redis://localhost:6379/0"
]
}
}
}Claude Code: terminal
claude mcp add redis -- uvx --from redis-mcp-server@latest redis-mcp-server --url redis://localhost:6379/0- Local server: stdio. PyPI: redis-mcp-server (uvx --from redis-mcp-server@latest redis-mcp-server --url redis://localhost:6379/0).
- Needs: --url connection string, or REDIS_HOST, REDIS_PORT, REDIS_DB, REDIS_USERNAME, REDIS_PWD; Optional TLS: REDIS_SSL and related cert vars; Optional Entra ID auth vars for Azure Managed Redis.
Security
Security considerations
-
Restrict with ACLs
Use a Redis user whose access control list allows only needed commands and keys.
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Not production sessions
Avoid connecting to instances that hold live sessions or tokens.
-
Review deletes
Ask the assistant to show keys before deleting anything.
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Log activity
Keep a record of commands run through the server.
Related servers
Servers that pair well with it
MongoDB
MongoDB's official MCP server lets AI query collections, run aggregations and manage Atlas clusters, with a read-only flag for safe use.
Qdrant
Qdrant's official MCP server gives AI a semantic memory: store information in a Qdrant collection and find it again by meaning.
Memory
The MCP project's Memory reference server gives AI a local knowledge graph to store people, things and facts and recall them later.
Keep exploring
Solutions and services that use it
Related services
View all related services- MCP integration Connect AI assistants to your CRM, files, databases and tools through Model Context Protocol servers, with safe permissions.
- Database design Data models that stay fast and correct as your business grows, with backups and access rules in place.
- AI Product Development AI agents, knowledge assistants, copilots and document automation built into the way your team already works.
- Web applications Custom browser-based software for the work your team does every day, from booking tools to operations systems.
- Dashboards and reporting Dashboards that pull numbers from the systems you already use and show what matters without a spreadsheet.
- Multi-tenant architecture One product serving many customer accounts, with each account's data kept separate and secure.
Solutions
View all solutions- Automated reporting Reports that build themselves from your systems on schedule, with a plain-language summary of what changed.
- Knowledge assistant (RAG) Ask a question in plain words and get an answer from your own documents, with links to the sources.
- Forecasting dashboards Forecast demand from your sales history and seasonality, and flag items to reorder before they run out.
- Internal help desk assistant An assistant in Slack or Teams that answers policy and how-to questions from your handbooks and opens tickets when needed.
- Payroll automation Turn pay rules, attendance and leave into tested software so the monthly run becomes a review and payslips go out in one batch.
- AI sales research agent Short, sourced briefings on each prospect before a call, drafted by an AI agent from public information and your CRM.
Industries
View all industriesCase studies
View all case studiesGuides and articles
View all guides and articles- How to connect AI to your database safely Let staff ask questions of your data in plain English without risking production systems or sensitive records.
- PostgreSQL vs MySQL The two most popular open-source databases compared for business applications.
- What is RAG and when do you need it? How retrieval-augmented generation lets AI answer from your documents, when it fits and how to build it well.
- How to automate reporting with AI and MCP Build reports that assemble themselves from your systems through MCP, with a written summary people can trust.
- How to set up automated reporting Replace manual weekly and monthly reports with dashboards and summaries that update themselves.
- RAG vs fine-tuning Two ways to make AI work with your knowledge, compared by cost, accuracy and upkeep.
AI models
View all ai models- text-embedding-3-large OpenAI's most capable embedding model for search and retrieval, with 3,072-dimension vectors and support for English and other languages.
- text-embedding-3-small OpenAI's efficient, lowest-cost embedding model, with 1,536-dimension vectors for search, retrieval and similarity at scale.
- Cohere Embed v4 Cohere's multimodal embedding model for enterprise search, embedding text, images and mixed documents, with flexible vector sizes and long inputs.
- Voyage 4 Voyage AI's current embedding series, now part of MongoDB, with large, standard and lite models that share one embedding space.
- Embeddings Models that turn text into vectors for search, retrieval, clustering and recommendations.
- Gemini Embedding 2 Google's current embedding model, multimodal: it embeds text, images, video, audio and PDFs for search and retrieval.
Glossary terms
View all glossary terms- Database A database is an organized store of data that software can search, update and keep consistent, such as customers, orders or bookings.
- RAG RAG, or retrieval-augmented generation, is a method where an AI system first finds relevant passages in your documents, then answers using only those passages.
- Vector database A vector database stores embeddings, lists of numbers that represent meaning, and quickly finds the ones most similar to a query.
- Embeddings Embeddings are lists of numbers that represent the meaning of text or images, so software can find items that are similar in meaning.
- Semantic search Semantic search finds results by meaning rather than exact words, so a search for "reset my login" can find an article titled "Forgot your password".
- Backend The backend is the part of an application that runs on servers, storing data, applying business rules and serving the frontend through APIs.
FAQ
Questions people ask us
Have a question that is not here? Ask us directly.
Yes. It is maintained by Redis.
We found none documented. Limit access with a Redis user and access control list.
Yes. It includes tools to list indexes and run vector searches.