How do I authenticate with Collibra?

This integration uses Basic Authentication with your Collibra instance URL, username, and password.

What is an asset in Collibra?

An asset is any cataloged object: a business term, a database table, a column, a report, or a data policy, among others.

Can I create new communities or domains?

The current version focuses on browsing metadata and creating assets. Community and domain creation may be added in future updates.

Collibra MCP Server for Pydantic AI 10 tools — connect in under 2 minutes

Name: Collibra
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 10 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Collibra through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 Collibra "
            "(10 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Collibra?"
    )
    print(result.data)

asyncio.run(main())

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* 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 Collibra MCP Server

Connect your AI to Collibra, the data intelligence platform that helps organizations find, understand, and trust their data.

Pydantic AI validates every Collibra tool response against typed schemas, catching data inconsistencies at build time. Connect 10 tools through 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

Asset Search — Search for data assets by name, type, or domain and retrieve their full metadata.
Community Browsing — List all communities and domains to navigate your data governance structure.
Asset Details — Inspect any asset's attributes, responsibilities, and relationships.

The Collibra 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 Collibra to Pydantic AI via MCP

Follow these steps to integrate the Collibra 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 Collibra with type-safe schemas

Why Use Pydantic AI with the Collibra MCP Server

Pydantic AI provides unique advantages when paired with Collibra 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 Collibra integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Collibra connection logic from agent behavior for testable, maintainable code

Collibra + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Collibra MCP Server delivers measurable value.

Type-safe data pipelines: query Collibra with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Collibra tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Collibra and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Collibra responses and write comprehensive agent tests

Collibra MCP Tools for Pydantic AI (10)

These 10 tools become available when you connect Collibra to Pydantic AI via MCP:

create_asset

Create a new asset in Collibra

get_asset

Retrieve detailed information about a specific asset

get_community_details

Retrieve detailed information about a specific community

list_asset_types

Retrieve a list of available asset types

list_assets

Retrieve a list of assets in Collibra

list_communities

Retrieve a list of communities in Collibra

list_domain_types

Retrieve a list of available domain types

list_domains

Retrieve a list of domains in Collibra

list_statuses

Retrieve a list of available asset statuses

search_assets

Search for assets by name

Example Prompts for Collibra in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Collibra immediately.

"Show me all communities in Collibra."

"Search for assets named 'Customer Data'."

"Who is the Data Steward assigned to the 'Product Inventory' asset?"

Troubleshooting Collibra MCP Server with Pydantic AI

Common issues when connecting Collibra to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Collibra + Pydantic AI FAQ

Common questions about integrating Collibra MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an 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?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Collibra MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.