Weights & Biases MCP Server for LlamaIndex 6 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Weights & Biases as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Weights & Biases. "
"You have 6 tools available."
),
)
response = await agent.run(
"What tools are available in Weights & Biases?"
)
print(response)
asyncio.run(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.
LlamaIndex agents combine Weights & Biases tool responses with indexed documents for comprehensive, grounded answers. Connect 6 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Weights & Biases MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 6 tools from Weights & Biases
Why Use LlamaIndex with the Weights & Biases MCP Server
LlamaIndex provides unique advantages when paired with Weights & Biases through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Weights & Biases tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Weights & Biases tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Weights & Biases, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Weights & Biases tools were called, what data was returned, and how it influenced the final answer
Weights & Biases + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Weights & Biases MCP Server delivers measurable value.
Hybrid search: combine Weights & Biases real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Weights & Biases to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Weights & Biases for fresh data
Analytical workflows: chain Weights & Biases queries with LlamaIndex's data connectors to build multi-source analytical reports
Weights & Biases MCP Tools for LlamaIndex (6)
These 6 tools become available when you connect Weights & Biases to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Weights & Biases to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpWeights & Biases + LlamaIndex FAQ
Common questions about integrating Weights & Biases MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 6 tools in under 2 minutes. No API key management needed.