Vectara MCP Server for Vercel AI SDK 7 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Vectara through 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 Vectara, 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 Vectara MCP Server
Connect your Vectara environment to any AI agent to unlock enterprise-grade Retrieval-Augmented Generation (RAG) and semantic search directly inside your conversational IDE or workspace.
The Vercel AI SDK gives every Vectara tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 7 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
- Semantic Search — Query your indexed private corpora naturally and return highly relevant, grounded documents without traditional keyword matching limitations.
- Conversational RAG — Execute fully-fledged interactive chats leveraging Vectara's backend to provide detailed, cited answers strictly based on your secure documents.
- Corpus Management — List all available data corpora, retrieve unique keys, and discover the shape of your indexed data environment on the fly.
- Document Auditing — Monitor specific document indexes within a corpus, verify correct ingestions, or permanently delete obsolete files avoiding polluted search results.
The Vectara MCP Server exposes 7 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 Vectara to Vercel AI SDK via MCP
Follow these steps to integrate the Vectara 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 7 tools from Vectara and passes them to the LLM
Why Use Vercel AI SDK with the Vectara MCP Server
Vercel AI SDK provides unique advantages when paired with Vectara 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 Vectara integration everywhere
Built-in streaming UI primitives let you display Vectara 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
Vectara + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Vectara MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Vectara in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Vectara tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Vectara capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Vectara through natural language queries
Vectara MCP Tools for Vercel AI SDK (7)
These 7 tools become available when you connect Vectara to Vercel AI SDK via MCP:
delete_corpus_document
This action is irreversible. Permanently removes a document from a corpus
execute_rag_chat
Provide corpus keys and the user query to get a summarized AI response with citations. Executes a RAG-powered chat completion
get_corpus_details
Retrieves metadata and configuration for a specific corpus
list_chat_sessions
Lists previous RAG chat sessions
list_corpora
Lists all corpora (searchable datasets) in the Vectara account
list_corpus_documents
Lists all indexed documents within a specific corpus
perform_semantic_search
Provide one or more comma-separated corpus keys and the query text. Executes a semantic search across one or more corpora
Example Prompts for Vectara in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Vectara immediately.
"List all configured knowledge corpora I have in Vectara."
"Query corpus `cor-81a` for instructions on 'rolling back kubernetes pods' and show only the top 3 best matching results."
"List all active chat context session IDs for the last week."
Troubleshooting Vectara MCP Server with Vercel AI SDK
Common issues when connecting Vectara to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpVectara + Vercel AI SDK FAQ
Common questions about integrating Vectara 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 Vectara 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 Vectara to Vercel AI SDK
Get your token, paste the configuration, and start using 7 tools in under 2 minutes. No API key management needed.