Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings), 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 Marqo AI (Vector Search & Embeddings) MCP Server
Connect your Marqo instance to any AI agent and take full control of your semantic search infrastructure, vector embeddings, and real-time document indexing through natural conversation.
The Vercel AI SDK gives every Marqo AI (Vector Search & Embeddings) 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
- Tensor Search Orchestration — Execute dense semantic similarity searches against your indices using natural language queries, with Marqo handling embedding extraction automatically
- Dynamic Document Ingestion — Write new JSON records into your vector indices directly from your agent, allowing for instant searchability of fresh data mappings
- Index Lifecycle Management — Create explicitly bounded new vector indices with custom model settings and dimension constraints to optimize your search architecture
- Vector Audit & Stats — Retrieve detailed configuration metrics for your indices, including document counts, embedding model types, and underlying schema mappings
- Precision Deletion — Physically eradicate vectorized representations by targeting specific scalar identifiers to maintain a clean and relevant search index
- Resource Inventory — List all available vector indices on your Marqo instance to identify collection boundaries before executing search queries
The Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) to Vercel AI SDK via MCP
Follow these steps to integrate the Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) and passes them to the LLM
Why Use Vercel AI SDK with the Marqo AI (Vector Search & Embeddings) MCP Server
Vercel AI SDK provides unique advantages when paired with Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) integration everywhere
Built-in streaming UI primitives let you display Marqo AI (Vector Search & Embeddings) 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
Marqo AI (Vector Search & Embeddings) + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Marqo AI (Vector Search & Embeddings) MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Marqo AI (Vector Search & Embeddings) in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Marqo AI (Vector Search & Embeddings) tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Marqo AI (Vector Search & Embeddings) capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Marqo AI (Vector Search & Embeddings) through natural language queries
Marqo AI (Vector Search & Embeddings) MCP Tools for Vercel AI SDK (6)
These 6 tools become available when you connect Marqo AI (Vector Search & Embeddings) to Vercel AI SDK via MCP:
add_documents
Write new documents into Marqo
create_index
Create an explicitly bounded new vector index
delete_documents
Delete specific documents from Marqo by targeting their IDs
get_index_stats
Get configuration and stats for an index
list_indexes
Crucial before writing queries hitting arbitrary collections. List all Marqo vector indexes
tensor_search
Perform natural language tensor search on Marqo
Example Prompts for Marqo AI (Vector Search & Embeddings) in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Marqo AI (Vector Search & Embeddings) immediately.
"Semantic search in index 'products' for 'lightweight running shoes for trails'"
"List all vector indexes in my Marqo instance"
"Add this document to the 'support-docs' index: {"title": "API Auth", "content": "Use Marqo-API-Key header"}"
Troubleshooting Marqo AI (Vector Search & Embeddings) MCP Server with Vercel AI SDK
Common issues when connecting Marqo AI (Vector Search & Embeddings) to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpMarqo AI (Vector Search & Embeddings) + Vercel AI SDK FAQ
Common questions about integrating Marqo AI (Vector Search & Embeddings) 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 Marqo AI (Vector Search & Embeddings) with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Google's framework for building production AI agents.
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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 Marqo AI (Vector Search & Embeddings) to Vercel AI SDK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.