Dataiku DSS MCP Server for Vercel AI SDK 14 tools — connect in under 2 minutes
The Vercel AI SDK is the TypeScript toolkit for building AI-powered applications. Connect Dataiku DSS 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 Dataiku DSS, 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 Dataiku DSS MCP Server
Connect your Dataiku DSS instance to any AI agent and take full control of your enterprise AI and collaborative data science workflows through natural conversation.
The Vercel AI SDK gives every Dataiku DSS tool full TypeScript type inference, IDE autocomplete, and compile-time error checking. Connect 14 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
- Project & Dataset Exploration — List all accessible DSS projects and retrieve structural extraction of dataset column schemas and types
- Pipeline Orchestration — Monitor build tasks and training runs by listing pipeline jobs and analyzing execution states and timing
- Transformation Auditing — Retrieve explicit configuration structures parsing precise Dataiku recipes (Python, SQL, Visual) to verify data logic
- Automation & Scenarios — List automation scenarios and trigger execution commands to rebuild pipelines or retrain models securely
- Model Monitoring — Identify saved ML models and retrieve detailed performance metrics defining specific trained schema layers
- Admin Oversight — Enumerate installed plugins and data connections (SQL, Cloud Storage, APIs) to verify organizational constraints
The Dataiku DSS MCP Server exposes 14 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 Dataiku DSS to Vercel AI SDK via MCP
Follow these steps to integrate the Dataiku DSS 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 14 tools from Dataiku DSS and passes them to the LLM
Why Use Vercel AI SDK with the Dataiku DSS MCP Server
Vercel AI SDK provides unique advantages when paired with Dataiku DSS 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 Dataiku DSS integration everywhere
Built-in streaming UI primitives let you display Dataiku DSS 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
Dataiku DSS + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Dataiku DSS MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Dataiku DSS in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Dataiku DSS tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Dataiku DSS capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Dataiku DSS through natural language queries
Dataiku DSS MCP Tools for Vercel AI SDK (14)
These 14 tools become available when you connect Dataiku DSS to Vercel AI SDK via MCP:
dataset_schema
Get the schema (columns, types) of a specific dataset
get_job
Get job state, timing, and outputs
get_model
Get saved model metadata, algorithm, and performance metrics
get_project
Get project metadata, settings, and tags
get_recipe
Get recipe configuration and settings
list_connections
List all DSS data connections (databases, cloud storage, APIs)
list_datasets
List all datasets in a project
list_jobs
List pipeline jobs in a project (build tasks, training runs)
list_models
List deployed/saved ML models in a project
list_plugins
List installed DSS plugins
list_projects
List all DSS projects accessible to the API key
list_recipes
List all recipes (data transformations) in a project
list_scenarios
List automation scenarios in a project
run_scenario
Trigger a scenario execution (build pipeline, retrain model)
Example Prompts for Dataiku DSS in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Dataiku DSS immediately.
"List all projects in my Dataiku instance"
"What is the schema for dataset 'raw_logs' in project 'FRAUD'?"
"Run scenario 'REBUILD_PIPELINE' in project 'SALES'"
Troubleshooting Dataiku DSS MCP Server with Vercel AI SDK
Common issues when connecting Dataiku DSS to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpDataiku DSS + Vercel AI SDK FAQ
Common questions about integrating Dataiku DSS 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 Dataiku DSS 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 Dataiku DSS to Vercel AI SDK
Get your token, paste the configuration, and start using 14 tools in under 2 minutes. No API key management needed.