AI assistants create more value when they can use trusted organisational context instead of relying on generic information. A Model Context Protocol (MCP) server is a small, specialised software program, that acts as a bridge between AI models and external data sources or tool. With the Host being an AI application or chat interface you interact with, and the Client that serves as the internal connector, embedded inside the AI host that manages the connection to the server, they allow LLMs to interact with external systems using (structured) natural language commands.
The Collibra Model Context Protocol (MCP) server enables this by making governed metadata available directly within AI conversations and agent workflows. This blog explains what the Collibra MCP server is, what organisations can do with it and which implementation choices require careful attention. Drawing on a practical comparison of an assistant based on an LLM and Databricks, it explores how MCP can support data product requests, strengthen Data Governance and connect business needs with reliable data. It also shows why technology alone is not enough: organisations need clear use cases, reliable metadata and carefully designed controls to turn the connection into lasting business value.
What is the Collibra MCP server?
Model Context Protocol, or MCP, is an open standard that connects AI applications to external data sources, tools and business systems. It provides a standard way for an AI assistant or agent to access external systems and information. You could see it as a standardised way where AI agents can ‘talk’ to each other. Specifically looking from a Collibra perspective, the Collibra MCP server applies this standard to the Collibra Data Intelligence Platform. It acts as a bridge between an AI application and the governed metadata stored in Collibra. The Collibra MCP server provides the AI assistant with a set of predefined capabilities, called tools. These tools allow the assistant to perform specific actions in Collibra, such as searching the Business Glossary, finding data assets, retrieving asset details or exploring data lineage. Some tools can also perform actions, such as creating or updating assets.
Instead of leaving an AI environment, searching the data catalogue and manually copying the result back, a user could ask: “Which certified dataset should I use to analyse customer retention, and where does its data come from?” The AI assistant could use Collibra to search for relevant assets, review their metadata and examine upstream lineage before formulating an answer. In practical terms, Collibra MCP makes governed context available inside an AI conversation or agent workflow.
Collibra currently provides two connection models. The hosted Collibra MCP server supports remote connections through OAuth. Collibra also provides an open-source local server that organisations can run themselves for development purposes or for MCP clients not covered by a featured integration. The local version uses a local transport and locally configured Collibra credentials.
What can you do with the MCP server?
The Collibra MCP server exposes both read and write tools. This means an AI assistant can retrieve context from Collibra and, when authorised, perform selected actions.
With the Collibra MCP server, users and AI agents can:
Find approved Business Terms and definitions
Users can ask questions about business terms and definitions using natural language. E.g., “What does active customer mean within our organisation?”
The assistant can search the Business Glossary and return the relevant governed definition rather than generating a generic explanation. This helps connect everyday language with agreed Business Terms. It can also reduce misunderstandings between business teams, analysts and technology teams.
Discover relevant and certified data assets
An AI assistant can search available data assets through natural language or keyword-based queries. E.g., “Which tables contain information about product complaints?”
The agent could identify relevant assets and retrieve detailed metadata, such as descriptions, attributes, tags, relationships and responsibilities. This makes the catalogue more accessible to people who may not know its structure, naming conventions or navigation options.
Explore data lineage
Collibra provides MCP tools for upstream and downstream technical lineage. An assistant can investigate where data originates, which transformations it passes through and which systems or reports consume it. E.g., “What would be affected if we changed this customer status field?”
The assistant could inspect downstream lineage to identify potential consumers and dependencies. This can support impact analysis, incident investigation, Data Quality management and change planning.
Connect business meaning to physical data
The available tools can trace Business Terms and measures to underlying tables and columns. They can also retrieve semantic information associated with tables and columns. This connection is particularly valuable because a technically correct table name does not automatically explain its business meaning. E.g., an agent could connect the term “net revenue” to an approved measure and then identify the physical columns used to calculate it.
This helps bridge business definitions, analytical logic and technical implementation.
Work with Data Contracts and classifications
The MCP tools can retrieve Data Contracts and their manifests. Authorised agents can also initialise contracts or upload updated manifests. Agents can search for Data Classes and classification matches.
Create and maintain assets
Write tools allow an authorised agent to create new Collibra assets and edit existing ones. Supported actions include updating attributes, changing an asset’s status, managing relationships, adding tags and assigning responsibilities such as Owner or Steward.
This creates opportunities for conversational metadata management. Write access also introduces additional risk. Organisations should therefore treat it differently from simple catalogue search.
Use Collibra context within data and AI platforms
Collibra’s current documentation lists featured connections for Databricks and Snowflake. Once connected, agents within these platforms can use Collibra tools to query catalogue information, explore lineage and perform authorised actions without leaving their native working environment.
This matters because Data Governance often loses value when it exists in a separate destination that users must remember to visit. MCP can bring governance context into the point where a data or AI decision is being made.
Best Practices
Connecting an AI assistant to Collibra is technically useful. Connecting it to poorly managed metadata will simply make poor metadata easier to access. A successful implementation therefore requires more than installing a server.
To create best practices for the Collibra MCP Server, we compared how an LLM and Databricks perform as AI assistants, and how they connect to a Collibra MCP Server, guiding users through data product requests. So again, this is based on a hands-on evaluation session.
Aspect | LLM: Recommended | Databricks: High build cost |
System Prompt | Supported: Works reliably and shapes the assistant’s behaviour throughout the session. | Partially supported: System prompt is possible but may be ignored during the conversation. |
Skills/ functions | Native – natural language: Skills are defined in plain language with a minimal prompt. Minimal alignment effort needed. Scales to enterprise. | Manual – code required: Functions can be built as skills equivalents, but require significant engineering effort per function. |
File handling | Supported: Files can be uploaded and used as context within the session. | Possible but very technical: Files can be uploaded and transformed into vector files that could be read by a tool in conversation with an LLM, but this requires significant engineering effort. |
Rate limits* | Not an issue in evaluation: Account token limits and pay per use apply, depending on the subscription of the LLM. | Rate limits apply: Can interrupt workflows in active use. We found these limits were hit after only a few questions in a conversation. |
Collibra context retrieval | Works via MCP: Automatically retrieves glossary terms, owners, and related assets from Collibra. Surfaces these as suggestions during the conversation. | Community scoping required: Must explicitly specify which community to search. Less automatic than LLM’s. |
Asset management | Check asset existence in skill: Recommended to validate whether an asset already exists before creating, and trigger an update if so. | Asset removal not possible: No tool available to remove an asset once created — a known limitation. |
Output quality/ UX | Conversational and guided: Back-and-forth dialogue, suggested next steps, and structured output come from the skill definition. Usable without additional UX work. | Very technical output: Raw output is not user-friendly. Significant UX work required before non-technical users can use it effectively. MCP query outputs are printed in full, cluttering the UI. |
Self-assigned status | Not observed | Assigns its own status: The assistant autonomously sets a status on assets, which can cause governance issues. |
Authentication | To be confirmed: Secure authentication method and global assignment scope need to be finalised. | Standard auth available |
Data use | Not connected to data sources: Can only read metadata from Collibra and local data based on desktop setup. | Direct connection to data: Possible to use data stored in Databricks in MCP conversation. This does require significant planning and engineering effort as SQL functions have to be written beforehand. |
*Rate limits are primarily determined by the LLM provider rather than by Collibra itself. The specific Collibra setup therefore has little impact on these limits. In platforms such as Databricks, larger or higher-tier environments will typically offer greater capacity and higher rate limits.
How does this create value for your organisation?
The Collibra MCP server helps organisations bring governed data context directly into AI supported workflows.
Instead of asking users to search Collibra manually, an AI assistant can retrieve relevant Business Terms, ownership information, related assets and other metadata while the user completes a task. In the data product request use case, this means the assistant can guide users through the required information, identify missing details and turn informal input into structured documentation.
This creates value in several ways.
Why involve Clever Republic?
The technology alone does not create value. An MCP connection can retrieve information from Collibra, but it still needs the right use case, reliable metadata, clear decision rules and a well designed process. Clever Republic connects these elements. We help organisations define where AI and Collibra can improve a real business process, rather than starting with the technology. We translate business needs into practical workflows, required metadata, governance controls and technical requirements. Our role is to make sure the solution works across data, people, process, technology and policy. This turns the Collibra MCP server from an interesting technical feature into a practical capability that delivers long term business value.
Clever Republic helps organisations move from connecting AI to Collibra towards embedding trusted Data Intelligence into real business decisions.
