Weights & Biases 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 Weights & Biases 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 Weights & Biases, 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 Weights & Biases MCP Server
Connect your Weights & Biases (WandB) account to any AI agent and manage your machine learning experiments through natural conversation.
The Vercel AI SDK gives every Weights & Biases 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
- Project Management — List all projects within your WandB entity (user or team) to browse your experiment folders
- Run Monitoring — List and track individual experiment runs within a project to monitor real-time activity
- Deep Run Analysis — Retrieve full details for any run, including latest accuracies, losses, and hyperparameters
- Artifact Management — List versioned datasets, models, and other artifacts to track data lineage and dependencies
- Sweep Tracking — Monitor automated hyperparameter search sweeps to see optimization progress
- Reports & Collaboration — List saved analysis reports and dashboards to access collaborative documentation
The Weights & Biases 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 Weights & Biases to Vercel AI SDK via MCP
Follow these steps to integrate the Weights & Biases 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 Weights & Biases and passes them to the LLM
Why Use Vercel AI SDK with the Weights & Biases MCP Server
Vercel AI SDK provides unique advantages when paired with Weights & Biases 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 Weights & Biases integration everywhere
Built-in streaming UI primitives let you display Weights & Biases 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
Weights & Biases + Vercel AI SDK Use Cases
Practical scenarios where Vercel AI SDK combined with the Weights & Biases MCP Server delivers measurable value.
AI-powered web apps: build dashboards that query Weights & Biases in real-time and stream results to the UI with zero loading states
API backends: create serverless endpoints that orchestrate Weights & Biases tools and return structured JSON responses to any frontend
Chatbots with tool use: embed Weights & Biases capabilities into conversational interfaces with streaming responses and tool call visibility
Internal tools: build admin panels where team members interact with Weights & Biases through natural language queries
Weights & Biases MCP Tools for Vercel AI SDK (6)
These 6 tools become available when you connect Weights & Biases to Vercel AI SDK via MCP:
get_run_details
Retrieves full details for a specific W&B run, including summary metrics and config
list_project_artifacts
Lists all artifacts (datasets, models, etc.) in a project
list_project_reports
Lists all saved analysis reports in a project
list_project_runs
Lists all experiment runs within a specific W&B project
list_project_sweeps
Lists hyperparameter search sweeps within a project
list_wandb_projects
Lists all projects within a Weights & Biases entity (user or team)
Example Prompts for Weights & Biases in Vercel AI SDK
Ready-to-use prompts you can give your Vercel AI SDK agent to start working with Weights & Biases immediately.
"List all runs in my 'transformer-nmt' project for entity 'ai-team'."
"Get the final accuracy and config for run ID 'vibrant-sweep-1'."
"What artifacts are available in the 'resnet-training' project?"
Troubleshooting Weights & Biases MCP Server with Vercel AI SDK
Common issues when connecting Weights & Biases to Vercel AI SDK through the Vinkius, and how to resolve them.
createMCPClient is not a function
npm install @ai-sdk/mcpWeights & Biases + Vercel AI SDK FAQ
Common questions about integrating Weights & Biases 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 Weights & Biases 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 Weights & Biases to Vercel AI SDK
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.