Redis Vector MCP Server
Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack.
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What is the Redis Vector MCP Server?
The Redis Vector MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Redis Vector via 6 tools. Equip your AI to autonomously manage embeddings, run KNN similarity searches, and administrate vector indexes natively inside your Redis stack. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (6)
Tools for your AI Agents to operate Redis Vector
Ask your AI agent "Search the index 'customer-support-vector' for the top 3 similar records to this embedding vector: [0.12, -0.45, 0.08, 0.99...]" and get the answer without opening a single dashboard. With 6 tools connected to real Redis Vector data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Redis Vector MCP Server capabilities
6 toolsSpecify the name and vector dimensions. Creates a new RediSearch vector index
Deletes a vector document from Redis
Retrieves details for a specific vector index
Lists all RediSearch vector indexes
Provide the query vector as a JSON array of floats. Performs a KNN similarity search in a vector index
Specify the document key and the vector as a JSON array. Inserts or updates a vector in a Redis hash
What the Redis Vector MCP Server unlocks
Connect your Redis database (equipped with the RediSearch module) to your AI agent, turning it into an advanced Vector Database administrator. Activating this integration grants your conversational interface the power to interact directly with your semantic search engine, enabling tasks like querying mathematical embeddings for similar records, configuring fresh vector indexes, and managing geometric data structures without needing dedicated external database clients.
What you can do
- Similarity Vector Search (KNN) — Let the AI perform rapid native vector comparisons (
search_vectors). Provide an embedding array via prompt or code, and retrieve the absolute nearesttop_kneighbors securely cached in your infrastructure. - Index Management — Actively discover all loaded RediSearch vector indexes, investigate their configured dimensions (
get_index_info), or command the AI to instantiate new KNN indexes (create_vector_index) tailored for fresh AI workloads. - Embedding Administration — Inject and modify geometric vector components associated with a document key (
upsert_vector), or purge legacy embeddings efficiently (delete_vector) to keep semantic records clean and operational.
How it works
1. Authorize the Redis Vector MCP connector from your module catalog.
2. Configure it securely by providing your full Redis URL (ensure it points to a Redis instance that natively supports RediSearch vector extensions).
3. Prompt your AI to "find the top 5 nearest neighbors for this JSON array in the 'products-index'" or "create a new 1536-dimensional vector index for OpenAI embeddings."
Who is this for?
- AI & ML Engineers — Rapidly iterate over similarity tuning. Store resulting chunk embeddings on the fly, and query KNN vectors right from the prompt instead of scripting Python drivers repeatedly.
- Backend Developers — Maintain semantic storage logic. Audit schemas, map out active index properties, and delete obsolete hashes holding raw vector models instantly.
- Data Architects — Validate your Redis vector environments interactively. Explore dimension structures and index readiness confirming architecture viability for RAG (Retrieval-Augmented Generation) applications.
Frequently asked questions about the Redis Vector MCP Server
What is the format required for the 'Redis URL' parameter?
The parameter requires standard Redis URI string formatting. Typically it looks like redis://[username]:[password]@[host]:[port]. For TLS/SSL-enabled endpoints spanning secure setups, use the rediss:// scheme prefix.
Does my Redis instance strictly need the RediSearch module?
Yes, absolutely. The base Redis product (standard open-source) only manages key-value caching out of the box. You must be running the Redis Stack or a managed tier (like Redis Enterprise or compatible cloud offerings) that explicitly includes RediSearch to generate and query KNN vector indexes.
Can I query using embedding arrays output directly from OpenAI models?
Yes. Once you receive your numerical float array from an embedding model (like text-embedding-ada-002), you can pipe that exact JSON array into the search_vectors agent tool alongside the relevant index name to perform immediate proximity lookups.
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Connect Redis Vector with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Give your AI agents the power of Redis Vector MCP Server
Production-grade Redis Vector MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.