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 LlamaIndex 10 tools — connect in under 2 minutes

Name: Collibra
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Collibra as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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 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 Collibra. "
            "You have 10 tools available."
        ),
    )

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

asyncio.run(main())

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About Collibra MCP Server

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

LlamaIndex agents combine Collibra tool responses with indexed documents for comprehensive, grounded answers. Connect 10 tools through 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

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 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 Collibra to LlamaIndex via MCP

Follow these steps to integrate the Collibra 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 10 tools from Collibra

Why Use LlamaIndex with the Collibra MCP Server

LlamaIndex provides unique advantages when paired with Collibra through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine Collibra tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain Collibra tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query Collibra, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what Collibra tools were called, what data was returned, and how it influenced the final answer

Collibra + LlamaIndex Use Cases

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

Hybrid search: combine Collibra real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query Collibra 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 Collibra for fresh data

Analytical workflows: chain Collibra queries with LlamaIndex's data connectors to build multi-source analytical reports

Collibra MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect Collibra to LlamaIndex 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 LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Collibra + LlamaIndex FAQ

Common questions about integrating Collibra MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query Collibra tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.