data.world MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect data.world through the Vinkius and every tool is automatically validated against Pydantic schemas — catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
agent = Agent(
model="openai:gpt-4o",
mcp_servers=[server],
system_prompt=(
"You are an assistant with access to data.world "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in data.world?"
)
print(result.data)
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.
Pydantic AI validates every data.world tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through the Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code — full type safety, structured output guarantees, and dependency injection for testable agents.
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 Pydantic AI 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 Pydantic AI via MCP
Follow these steps to integrate the data.world MCP Server with Pydantic AI.
Install Pydantic AI
Run pip install pydantic-ai
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 10 tools from data.world with type-safe schemas
Why Use Pydantic AI with the data.world MCP Server
Pydantic AI provides unique advantages when paired with data.world through the Model Context Protocol.
Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
Model-agnostic architecture — switch between OpenAI, Anthropic, or Gemini without changing your data.world integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your data.world connection logic from agent behavior for testable, maintainable code
data.world + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the data.world MCP Server delivers measurable value.
Type-safe data pipelines: query data.world with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple data.world tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query data.world and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock data.world responses and write comprehensive agent tests
data.world MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect data.world to Pydantic AI 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 Pydantic AI
Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI
Common issues when connecting data.world to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aidata.world + Pydantic AI FAQ
Common questions about integrating data.world MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.Does Pydantic AI validate MCP tool responses?
Can I switch LLM providers without changing MCP code?
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 Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.