Weights & Biases MCP Server
Track experiments, monitor ML runs, and manage artifacts on WandB — the developer platform for AI.
Ask AI about this MCP Server
Vinkius supports streamable HTTP and SSE.
* 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
What is the Weights & Biases MCP Server?
The Weights & Biases MCP Server gives AI agents like Claude, ChatGPT, and Cursor direct access to Weights & Biases via 6 tools. Track experiments, monitor ML runs, and manage artifacts on WandB — the developer platform for AI. Powered by the Vinkius - no API keys, no infrastructure, connect in under 2 minutes.
Built-in capabilities (6)
Tools for your AI Agents to operate Weights & Biases
Ask your AI agent "List all runs in my 'transformer-nmt' project for entity 'ai-team'." and get the answer without opening a single dashboard. With 6 tools connected to real Weights & Biases data, your agents reason over live information, cross-reference it with other MCP servers, and deliver insights you would spend hours assembling manually.
Works with Claude, ChatGPT, Cursor, and any MCP-compatible client. Powered by the Vinkius - your credentials never touch the AI model, every request is auditable. Connect in under two minutes.
Why teams choose Vinkius
One subscription gives you access to thousands of MCP servers - and you can deploy your own to the Vinkius Edge. Your AI agents only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure and security, zero maintenance.
Build your own MCP Server with our secure development framework →Vinkius works with every AI agent you already use
…and any MCP-compatible client
Weights & Biases MCP Server capabilities
6 toolsRetrieves full details for a specific W&B run, including summary metrics and config
Lists all artifacts (datasets, models, etc.) in a project
Lists all saved analysis reports in a project
Lists all experiment runs within a specific W&B project
Lists hyperparameter search sweeps within a project
Lists all projects within a Weights & Biases entity (user or team)
What the Weights & Biases MCP Server unlocks
Connect your Weights & Biases (WandB) account to any AI agent and manage your machine learning experiments through natural conversation.
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
How it works
1. Subscribe to this server
2. Enter your WandB API Key and optional Base URL
3. Start managing your ML experiments through Claude, Cursor, or any MCP-compatible client
No more manual browsing through complex experiment dashboards to check model performance. Your AI agent becomes your ML research assistant.
Who is this for?
- Machine Learning Engineers — monitor model training progress and compare hyperparameters across multiple runs
- Data Scientists — track data lineage and manage versioned artifacts for reproducible research
- Research Teams — collaborate on experiment reports and monitor shared project sweeps through chat
- AI Developers — quickly surface model metrics and experiment summaries for faster development cycles
Frequently asked questions about the Weights & Biases MCP Server
Can I check the latest metrics for a specific ML run?
Yes. Using the get_run_details tool, your AI agent can pull the latest logged metrics (like accuracy or loss) and hyperparameters for any specific run ID within your projects.
Is it possible to list versioned datasets and models?
Absolutely. The list_project_artifacts tool allows you to see all artifacts, including datasets and models, helping you track data lineage and versioning directly through conversation.
Can I monitor hyperparameter search sweeps via chat?
Yes. Use the list_project_sweeps tool to monitor automated optimization tasks. Your agent will return a list of sweeps in the project so you can track progress without leaving your workspace.
More in this category
LlamaCloud (Managed RAG & Parsing)
6 toolsManage RAG pipelines and document parsing via LlamaCloud — orchestrate LlamaParse jobs and audit data ingestion.
fal.ai 3D
12 toolsGenerate 3D models via fal.ai — convert images and text to 3D assets using Rodin, TripoSR, Trellis, and 9+ AI models from any AI agent.
Vapi
10 toolsCommand Voice AI assistants directly from your chat. Make outbound phone calls, update personas, and retrieve full transcripts via Vapi.
You might also like
The Movie Database (TMDb)
15 toolsSearch movies, TV shows, people, get details, cast, trailers, and discover content via TMDb API.
Endorsal Testimonials
10 toolsEquip your AI agent to manage testimonials, track display widgets, and approve reviews via the Endorsal API.
Little Green Light
9 toolsManage donor constituents, gifts, and appeals via the Little Green Light REST API.
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.
Give your AI agents the power of Weights & Biases MCP Server
Production-grade Weights & Biases MCP Server. Verified, monitored, and maintained by Vinkius. Ready for your AI agents — connect and start using immediately.