Conduit MCP Server for Vercel AI SDK 8 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Conduit 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 Conduit, 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 Conduit MCP Server
Connect your AI agent seamlessly with Conduit, the modern data integration and synchronization platform. Utilizing natural language interactions, users can instruct the AI to oversee active streaming health, check connectors, and extract pipeline logs without accessing the conventional web dashboard interfaces.
The Vercel AI SDK gives every Conduit tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 8 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
- Pipeline Management — Request status overviews of active, paused, or degraded data integration pipelines efficiently.
- Connector Auditing — Ask the agent to locate specific connectors (source or destination) mapped to your critical infrastructure.
- Log Evaluation — Fetch recent application logs or streaming output reports via conversation to debug integration errors on the fly.
The Conduit MCP Server exposes 8 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 Conduit to Vercel AI SDK via MCP
Follow these steps to integrate the Conduit 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 8 tools from Conduit and passes them to the LLM
Why Use Vercel AI SDK with the Conduit MCP Server
Vercel AI SDK provides unique advantages when paired with Conduit 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 Conduit integration everywhere
Built-in streaming UI primitives let you display Conduit 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
Conduit + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Conduit MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Conduit in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Conduit tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Conduit capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Conduit through natural language queries
Conduit MCP Tools for Vercel AI SDK (8)
These 8 tools become available when you connect Conduit to Vercel AI SDK via MCP:
get_run_status
Returns detailed status, timing, and error information. Retrieve the current status of a specific workflow run
get_workflow
Returns source, destination, and current status. Retrieve detailed information about a specific workflow
list_available_destinations
Retrieve available data destination connector types supported by Conduit
list_available_sources
Retrieve available data source connector types supported by Conduit
list_connections
Retrieve a list of all active source and destination connections
list_workflow_runs
Returns the execution history with status and timestamps for each run. Retrieve the history of runs for a specific workflow
list_workflows
Use this as a starting point to discover workflow IDs for subsequent operations. Retrieve a list of all data integration workflows in Conduit
trigger_workflow
Use list_workflows first to find the workflow ID. Manually trigger a run for a specific workflow
Example Prompts for Conduit in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Conduit immediately.
"Retrieve the current status of all major pipelines running in the production Conduit instance."
"Check if there's a configured destination connector named 's3-analytics-bucket' and briefly describe its configuration parameters."
"Pause the pipeline 'MySQL-to-Kafka' immediately."
Troubleshooting Conduit MCP Server with Vercel AI SDK
Common issues when connecting Conduit to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpConduit + Vercel AI SDK FAQ
Common questions about integrating Conduit 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 Conduit 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 Conduit to Vercel AI SDK
Get your token, paste the configuration, and start using 8 tools in under 2 minutes. No API key management needed.