Tolstoy MCP Server for Vercel AI SDKGive Vercel AI SDK instant access to 6 tools to Get Video Analytics, List Folders, List Interactive Projects, and more
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Tolstoy 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 App Connector for Vercel AI SDK
The Tolstoy app connector for Vercel AI SDK is a standout in the Ecommerce category — giving your AI agent 6 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
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 Tolstoy, 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 Tolstoy MCP Server
Connect your Tolstoy interactive video account to any AI agent and simplify how you build personalized video experiences, manage your media library, and track engagement through natural conversation.
The Vercel AI SDK gives every Tolstoy 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
- Video Management — List all uploaded videos and programmatically import new content via external URLs.
- Interactive Projects — Query and manage your branching video flows and personalized interactive experiences.
- Performance Tracking — Retrieve detailed analytics including plays, conversion rates, and revenue impact for your videos.
- Media Organization — List and oversee video folders to keep your marketing assets structured.
- Event Monitoring — List configured webhooks to ensure your real-time video notifications are active.
- Engagement Insights — Fetch high-level summaries of how users are interacting with your video content.
The Tolstoy 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.
All 6 Tolstoy tools available for Vercel AI SDK
When Vercel AI SDK connects to Tolstoy through Vinkius, your AI agent gets direct access to every tool listed below — spanning interactive-video, shoppable-video, video-marketing, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Get performance metrics
List video folders
List interactive video projects
List your Tolstoy videos
List configured webhooks
Upload a new video to Tolstoy
Connect Tolstoy to Vercel AI SDK via MCP
Follow these steps to wire Tolstoy into Vercel AI SDK. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
npm install @ai-sdk/mcp ai @ai-sdk/openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the script
agent.ts and run with npx tsx agent.tsExplore tools
Why Use Vercel AI SDK with the Tolstoy MCP Server
Vercel AI SDK provides unique advantages when paired with Tolstoy 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 Tolstoy integration everywhere
Built-in streaming UI primitives let you display Tolstoy 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
Tolstoy + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Tolstoy MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Tolstoy in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Tolstoy tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Tolstoy capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Tolstoy through natural language queries
Example Prompts for Tolstoy in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Tolstoy immediately.
"List all videos in my Tolstoy library."
"Show me the analytics summary for my videos."
"Upload this video to Tolstoy: 'https://vinkius.com/intro.mp4' with name 'Introduction v2'."
Troubleshooting Tolstoy MCP Server with Vercel AI SDK
Common issues when connecting Tolstoy to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpTolstoy + Vercel AI SDK FAQ
Common questions about integrating Tolstoy 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.