Redis Vector MCP Server for Vercel AI SDK 6 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Redis Vector through the Vinkius and every tool is available as a typed function — ready for React Server Components, API routes, or any Node.js backend.
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Vinkius supports streamable HTTP and SSE.
import { createMCPClient } from "@ai-sdk/mcp";
import { generateText } from "ai";
import { openai } from "@ai-sdk/openai";
async function main() {
const mcpClient = await createMCPClient({
transport: {
type: "http",
// Your Vinkius token — get it at cloud.vinkius.com
url: "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
},
});
try {
const tools = await mcpClient.tools();
const { text } = await generateText({
model: openai("gpt-4o"),
tools,
prompt: "Using Redis Vector, list all available capabilities.",
});
console.log(text);
} finally {
await mcpClient.close();
}
}
main();
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Redis Vector MCP Server
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.
The Vercel AI SDK gives every Redis Vector tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 6 tools through the Vinkius and stream results progressively to React, Svelte, or Vue components — works on Edge Functions, Cloudflare Workers, and any Node.js runtime.
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.
The Redis Vector MCP Server exposes 6 tools through the Vinkius. Connect it to Vercel AI SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Redis Vector to Vercel AI SDK via MCP
Follow these steps to integrate the Redis Vector MCP Server with Vercel AI SDK.
Install dependencies
Run npm install @ai-sdk/mcp ai @ai-sdk/openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the script
Save to agent.ts and run with npx tsx agent.ts
Explore tools
The SDK discovers 6 tools from Redis Vector and passes them to the LLM
Why Use Vercel AI SDK with the Redis Vector MCP Server
Vercel AI SDK provides unique advantages when paired with Redis Vector through the Model Context Protocol.
TypeScript-first: every MCP tool gets full type inference, IDE autocomplete, and compile-time error checking out of the box
Framework-agnostic core works with Next.js, Nuxt, SvelteKit, or any Node.js runtime — same Redis Vector integration everywhere
Built-in streaming UI primitives let you display Redis Vector tool results progressively in React, Svelte, or Vue components
Edge-compatible: the AI SDK runs on Vercel Edge Functions, Cloudflare Workers, and other edge runtimes for minimal latency
Redis Vector + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Redis Vector MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Redis Vector in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Redis Vector tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Redis Vector capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Redis Vector through natural language queries
Redis Vector MCP Tools for Vercel AI SDK (6)
These 6 tools become available when you connect Redis Vector to Vercel AI SDK via MCP:
create_vector_index
Specify the name and vector dimensions. Creates a new RediSearch vector index
delete_vector
Deletes a vector document from Redis
get_index_info
Retrieves details for a specific vector index
list_indexes
Lists all RediSearch vector indexes
search_vectors
Provide the query vector as a JSON array of floats. Performs a KNN similarity search in a vector index
upsert_vector
Specify the document key and the vector as a JSON array. Inserts or updates a vector in a Redis hash
Example Prompts for Redis Vector in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Redis Vector immediately.
"Search the index 'customer-support-vector' for the top 3 similar records to this embedding vector: [0.12, -0.45, 0.08, 0.99...]"
"Insert a new embedding into the database with the key 'user:439:preference' containing the vector `[0.2, -0.1...]`."
"Retrieve the index information logic and schema mapping for 'docs-semantic-index'."
Troubleshooting Redis Vector MCP Server with Vercel AI SDK
Common issues when connecting Redis Vector to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpRedis Vector + Vercel AI SDK FAQ
Common questions about integrating Redis Vector MCP Server with Vercel AI SDK.
How does the Vercel AI SDK connect to MCP servers?
createMCPClient from @ai-sdk/mcp and pass the server URL. The SDK discovers all tools and provides typed TypeScript interfaces for each one.Can I use MCP tools in Edge Functions?
Does it support streaming tool results?
useChat and streamText that handle tool calls and display results progressively in the UI.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.
Connect Redis Vector to Vercel AI SDK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.