How Atlan MCP Server Builds Context For Your AI Tools

What can you do with Atlan MCP? #

Summarize and analyze this article with 👉 🔮 Google AI Mode or 💬 ChatGPT or 🔍 Perplexity or 🤖 Claude or 🐦 Grok (X) .

The Atlan MCP server acts as a bridge between Atlan’s metadata platforms and AI tools. This enables other AI agents to:

Search assets: Search for assets in Atlan using filters like name, type, tags, or domains.
Query by DSL: Retrieve specific assets using Atlan’s DSL query language.
Explore data lineage: Explore upstream or downstream lineage for a given asset.
Update assets: Modify asset metadata, including descriptions and certification status.

Common Atlan MCP uses

Common Atlan MCP uses - Image by Atlan.

Why do you need the Atlan MCP server? #

Modern data teams and AI agents face critical roadblocks that decrease speed to insights and trust in data. Common problems faced include:

Users (and their AI agents) lack the context needed to use data effectively
Limited technical expertise can make asset discovery difficult
Metadata stays underused due to tool friction
Constant context and tool switching disrupts data-driven decision-making
AI agents risk hallucinating or giving inaccurate responses without proper metadata context

That’s where Atlan MCP can help. MCP is an open standard that standardizes how AI agents communicate with software systems. So, instead of guessing, your AI knows exactly how to ask questions and take action.

MCP embeds AI in the way your organization discovers, governs, and activates data. So, with Atlan MCP, you can bring AI into your metadata workflows, without sacrificing control or context.

Learn how to set up Atlan MCP →

How does the Atlan MCP server work? #

Using Atlan metadata as a foundation, the Atlan MCP (Model Context Protocol) server allows you to connect Atlan to your favorite AI tool, like Cursor, Claude Desktop, etc.

“Just as USB-C provides a standardized way to connect your devices to various peripherals and accessories, MCP provides a standardized way to connect AI models to different data sources and tools.” - MCP Server documentation

Atlan manages AI and data governance for the MCP server out of the box, making it a secure way to use conversational AI across your organization.

One of the key concepts to understand the inner workings of Atlan MCP server is context engineering. Let’s see how Atlan MCP server enables it.

What is context engineering, and how does Atlan MCP enable it? #

Context engineering is both a superset and an evolution of prompt engineering. It is more than just writing good prompts using clear instructions. This involves:

Understanding the capabilities and token constraints of the large language model (LLM)
Utilizing model features, such as long-term and short-term memories, rules, etc.
Including, excluding, and ordering the information to provide to the prompt
Dynamically enriching prompts using tools like MCP, ACP, and A2A

In other words, context engineering is about finding the correct information and structure to provide to an LLM within the limits of the context window.

This information can come in many forms, such as memories, instructions, and tools.

Categories of context engineering

Categories of context engineering - Source: Langchain.

Atlan MCP enables context engineering by supplying dynamic, tool-generated context—crucial for improving model outputs.

For example, you can use the Atlan MCP server to interact with the rich layer of metadata stored and managed in Atlan’s unified control plane of data.

Besides cataloging information, it also provides the operational and semantic knowledge of the organization. In other words, Atlan’s MCP can enable other MCP tools within your organization as it is the storehouse of all your organization’s metadata.

Gartner’s Inaugural Magic Quadrant for D&A Governance is Here #

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Read the Magic Quadrant for D&A Governance

How does Atlan MCP interact with other MCP servers? #

Atlan’s MCP server is currently configured to be used in one of the three following environments:

Cursor
Claude Desktop
Your local environment

If you’re using Cursor or Claude Desktop, you can set Atlan MCP up by using the uv package manager or Docker.

Once set up, you can connect to your Atlan deployment from your AI code editor or assistant and fulfill the following use cases:

Enrich your model’s context with the structural, semantic, and other types of metadata you get from Atlan
Use AI as an intermediary to enable a no-code integration layer between Atlan and any other MCP tool you have

When you’re using Atlan MCP for enriching your model’s context, you’re using it as an AI-to-tool interface.

On the other hand, when you’re using Atlan’s MCP to communicate with the MCP servers of databases, applications, and other tools, you’re using AI as a mere facilitator for the communication. The latter is not an established pattern, but you can nevertheless use it with caution.

Learn how to set up Atlan MCP →

How does Atlan MCP enable conversational intelligence? #

An MCP server helps your AI tools work better by adding relevant context to your AI code editor or environment, improving development and process workflows.

Consider the MCP server for Amazon S3. It allows users to:

Create and manage S3 namespaces and buckets
Import data from sources using the COPY command
Query the data stored in S3 files

You can achieve similar functionality with Atlan. You can ask questions like:

Can you find all the data assets that have the tag campaign and were created or updated in the last two months?
Give me the most used data assets by query volume in the last two weeks.
Identify all the data assets that are frequently used but are not certified.
Can you list all the data assets (tables and columns) in Snowflake that don’t have any description?

The ability to ask these questions directly from the conversational interface will make it very easy to search, discover, trace lineage, and monitor quality for business users, engineers, and analysts alike.

Which Atlan features are enabled by Atlan MCP Server support? #

Atlan MCP gives you access to key data catalog features like search, lineage, data quality monitoring, and trust-building.

So, the following tools will be accessible to you using your MCP interface in Claude Desktop, Cursor, or your local environment:

Search and discovery: An Amazon-like search and filtering experience for all your data assets using the get_assets_by_dsl and search_assets tools.
Lineage and impact analysis: Understand dependencies, impact, etc. using the traverse_lineage tool.
Update metadata from other tools: At this point, updating userDescription, certificateStatus, and readme is allowed using the update_assets tool.

All of Atlan’s MCP features and tools are implemented using Atlan’s open-source Agent Toolkit, which you can use to develop or modify Atlan’s tools for your MCP server.

Discover how a modern data governance platform drives real results

Book a Personalized Demo →

How can you deploy Atlan MCP server with your AI tool? #

The process of deploying your own MCP server with Atlan involves the following steps:

Generate Atlan API key by following the documentation
Install via Docker - Uses Docker containers (recommended)
Add config to Claude Desktop, Cursor

Once you do that, you’ll have the following tools available for use:

Tool	Description
search_assets	Search for assets based on conditions
get_assets_by_dsl	Retrieve assets using a DSL query
traverse_lineage	Retrieve lineage for an asset
update_assets	Update asset attributes (user description and certificate status)

You can install the Atlan MCP server using uv too, although using Docker is the recommended method.

Ready to unlock the full potential of your data with Atlan MCP Server? #

The world of data has shifted significantly towards leveraging generative AI with use cases like AI-driven data engineering and governance, and generative BI, among others.

Databases, data warehouses, and BI tools have their own MCP servers, but none of the aforementioned use cases can truly realize their full potential without leveraging metadata.

That’s where the Atlan MCP server comes into the picture. It brings the same metadata foundation that you use for metadata activation through the unified control plane to your AI-driven development and business workflows.

Discover how a modern data governance platform drives real results

Book a Personalized Demo →

FAQs about Atlan MCP Server #

1. What is MCP (Model Context Protocol)? #

Model Context Protocol is an open protocol for providing context to LLMs. The goal is to give users more context so the LLM can generate better responses.

MCP has an architecture that works on integrations with various types of applications, where specific features of those applications are exposed as tools, using which context can be extracted.

The MCP layer is very similar to an API layer, but is significantly simpler and easier to use.

2. What is the Atlan MCP Server? #

The Atlan MCP (Model Context Protocol) Server connects Atlan to AI tools like Cursor and Claude, enabling AI-powered search, lineage, and governance by injecting metadata context into your AI workflows.

3. How is Atlan MCP different from other MCP servers? #

While other MCP servers connect databases or BI tools, Atlan MCP provides a metadata layer that enhances them all—making it foundational for secure, AI-driven data discovery and governance.

4. What is context engineering? #

If prompt engineering involves structuring and formatting the prompt in the right way to get the most out of the model inference, context engineering involves getting the most relevant and dynamic information in the first place.

MCP offers a way to do that by providing a no-code (using pre-built connectors) interface to connect to tools that help you enrich the context dynamically.

For example, suppose you want to make a database call or fetch some metadata on the validity of a data asset. In that case, you’d be able to get that information and supply it as context to the LLM using a database MCP server and a catalog MCP server, respectively.

5. How does Atlan MCP help improve AI outputs? #

By supplying relevant, real-time metadata context—such as lineage, asset tags, and usage patterns—Atlan MCP helps AI agents avoid hallucinations and produce accurate, context-rich responses.

6. How can you use AI in data engineering workflows? #

Integrating AI into data engineering needs context. That’s where MCP helps. Examples include:

DuckDB’s MCP server helping with faster data pipelines
dbt’s MCP server for running dbt models and querying data
AWS’s MCP server for accelerating the speed of development for data pipelines

At the core of enabling these other MCP-led development patterns is Atlan’s MCP server, which can provide the most relevant context about existing data assets from the unified control plane.

Earlier, you could only activate metadata in Atlan using direct integrations using APIs and connectors; now, you can do that by leveraging MCP and LLMs, too.

7. Which tools can I connect to Atlan MCP? #

You can deploy Atlan MCP with tools like Claude Desktop, Cursor, or your own local AI environment using Docker or uv package manager.

8. What metadata actions can I perform through Atlan MCP? #

Atlan MCP supports asset search, lineage tracing, DSL-based queries, and updating metadata fields like descriptions and certification status—all from your conversational AI interface.

Atlan MCP development is happening at a fast pace, which is why the following resources might be useful to anchor your understanding in the fundamentals, get acquainted with the playbooks and toolkits, and also keep an eye out for the latest updates from Atlan.