OpenSearch 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 OpenSearch Vector through Vinkius and every tool is available as a typed function. ready for React Server Components, API routes, or any Node.js backend.
ASK AI ABOUT THIS MCP SERVER
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 OpenSearch 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 OpenSearch Vector MCP Server
Turn your OpenSearch cluster into an AI-native vector database. Create k-NN indexes, upsert embeddings, run similarity searches, and inspect index configurations — all through natural conversation with your AI agent.
The Vercel AI SDK gives every OpenSearch Vector tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 6 tools through 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
- Vector Search — Execute k-Nearest Neighbors queries against any k-NN index with custom top-K limits and dense float vectors
- Index Management — List all cluster indexes with health status and document counts, or inspect a specific index's vector dimension, engine config, and distance metric
- Create Index — Provision new k-NN indexes optimized for cosine similarity with configurable vector dimensions (384, 768, 1536, etc.)
- Document Operations — Upsert vector documents with metadata, or delete documents from the embedding space by ID
The OpenSearch 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 OpenSearch Vector to Vercel AI SDK via MCP
Follow these steps to integrate the OpenSearch 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 OpenSearch Vector and passes them to the LLM
Why Use Vercel AI SDK with the OpenSearch Vector MCP Server
Vercel AI SDK provides unique advantages when paired with OpenSearch 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 OpenSearch Vector integration everywhere
Built-in streaming UI primitives let you display OpenSearch 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
OpenSearch Vector + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the OpenSearch Vector MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query OpenSearch Vector in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate OpenSearch Vector tools and return structured JSON responses to any frontend
Chatbots with tool use: embed OpenSearch Vector capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with OpenSearch Vector through natural language queries
OpenSearch Vector MCP Tools for Vercel AI SDK (6)
These 6 tools become available when you connect OpenSearch Vector to Vercel AI SDK via MCP:
create_index
knn: true` and mapping a rigid dynamic dense vector field optimized for cosine similarity. Create a new native OpenSearch KNN index ready for vector embeddings
delete_document
Delete an explicit vector document bounding from OpenSearch
get_index
Retrieve explicit OpenSearch index mapping and settings
index_document
This executes a fast transactional atomic insertion into the embedding space. Upsert a singular vector document directly into an OpenSearch KNN index
list_indexes
List all explicit indexes residing on the OpenSearch cluster
search
Provide the exact index name and a JSON-stringified dense float vector array to find conceptually similar embeddings natively. Execute a K-Nearest Neighbors (k-NN) vector search against OpenSearch
Example Prompts for OpenSearch Vector in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with OpenSearch Vector immediately.
"List all vector indexes in my OpenSearch cluster."
"Find the 5 most similar documents to this embedding in the knowledge-base index."
"Create a new k-NN index called 'customer-feedback' with 1536 dimensions."
Troubleshooting OpenSearch Vector MCP Server with Vercel AI SDK
Common issues when connecting OpenSearch Vector to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpOpenSearch Vector + Vercel AI SDK FAQ
Common questions about integrating OpenSearch 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 OpenSearch 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 OpenSearch 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.