data.world MCP Server for LangChain 10 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect data.world through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"dataworld": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using data.world, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
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 data.world MCP Server
Integrate data.world, the enterprise data catalog and collaborative data platform, directly into your AI workflow. Discover datasets, monitor data projects, and explore saved queries and insights using natural language.
LangChain's ecosystem of 500+ components combines seamlessly with data.world through native MCP adapters. Connect 10 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Data Discovery — Search the entire data.world catalog for relevant datasets and projects.
- Asset Management — List and retrieve detailed information for datasets and projects you own or manage.
- Collection Oversight — Explore curated collections of data assets within your organization.
- Query & Insight Access — List saved SQL/SPARQL queries and published insights for your data projects.
The data.world MCP Server exposes 10 tools through the Vinkius. Connect it to LangChain 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 data.world to LangChain via MCP
Follow these steps to integrate the data.world MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 10 tools from data.world via MCP
Why Use LangChain with the data.world MCP Server
LangChain provides unique advantages when paired with data.world through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine data.world MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across data.world queries for multi-turn workflows
data.world + LangChain Use Cases
Practical scenarios where LangChain combined with the data.world MCP Server delivers measurable value.
RAG with live data: combine data.world tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query data.world, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain data.world tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every data.world tool call, measure latency, and optimize your agent's performance
data.world MCP Tools for LangChain (10)
These 10 tools become available when you connect data.world to LangChain via MCP:
get_dataset_details
Includes field definitions, file listings, tags, and license information. Get detailed information for a specific dataset
get_my_profile
world. Returns profile attributes including username, display name, and account-level permissions. Retrieve metadata for the current authenticated user
get_project_details
Resolves project members, associated objective, and the current status of linked resources. Get detailed information for a specific project
list_dataset_queries
Returns a list of saved query definitions, including their language (SQL/SPARQL) and creator metadata. List all saved SQL or SPARQL queries for a dataset
list_my_collections
Returns collection identifiers, descriptions, and item counts. List all collections you own or manage
list_my_datasets
world catalog for datasets owned by the authenticated user. Returns a list of dataset metadata including title, visibility (public/private), and timestamp of last modification. List all datasets you own on data.world
list_my_projects
Returns project summaries including role (owner/contributor), creation date, and linked datasets. List all projects you own on data.world
list_project_insights
Insights represent documented findings and data visualizations attached to the project workspace. List all insights published within a project
list_recent_activity
Returns a stream of activity logs including dataset updates, project contributions, and new collection entries. List recent activities in your data.world account
search_catalog
world index. Supports full-text search across titles, descriptions, and tags. Returns a ranked list of matching resources. Search for datasets and projects across data.world
Example Prompts for data.world in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with data.world immediately.
"Search for datasets related to 'Global Climate Change'."
"List all SQL queries saved in my project 'Sales Analysis 2024'."
"Show me the insights published in dataset 'Retail Trends'."
Troubleshooting data.world MCP Server with LangChain
Common issues when connecting data.world to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersdata.world + LangChain FAQ
Common questions about integrating data.world MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect data.world 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 data.world to LangChain
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.