Whether it’s answering questions, automating tasks, or driving business decisions, modern data teams are embracing AI assistants to move faster. But these tools often fall short when it comes to context.

AI agents don’t know where your trusted data lives. They can’t trace relationships across assets or understand how changes will ripple through your stack. Without access to metadata, they’re unable to support the complex, interconnected decisions that data teams make every day.

As organizations scale, they don’t just need AI that’s powerful, they need AI that’s deeply context-aware.

That’s why we built the Atlan MCP Server: to bring structured, secure metadata context directly to your AI agents and unlock smarter, more scalable decision-making.

What is the Atlan MCP?

The Atlan MCP Server is a powerful new way to bring AI into your metadata workflows, without sacrificing control or context.

Built on the Model Context Protocol (MCP), it gives your favorite AI tools, like Claude, Cursor, or your own copilots, the ability to interact with Atlan like a human would. Think of it as giving your AI assistant a set of functions it can call, so it knows how to search, filter, and analyze metadata on demand.

If you’ve never encountered MCP before, here’s a quick primer: MCP is an open standard that lets AI agents communicate with software systems through structured function calls. It standardizes how agents talk to tools. So instead of guessing, your AI knows exactly how to ask questions and take action.

For example, you could ask:

• “What are our top 10 certified data sources for revenue analysis?”
• “Which columns were updated last week in the churn dashboard?”
• “Show me all assets connected to our customer satisfaction score.”

With Atlan MCP, your agent can answer these questions in seconds. It queries your catalog, understands lineage, and applies filters, just like an expert data steward would.

This is a major step in our journey toward an AI-native data experience, where AI isn’t just layered on top of your tools, but embedded in the way your organization discovers, governs, and activates data.

What Can AI Agents Do with Atlan MCP?

Once connected to the Atlan MCP Server, your AI agent becomes metadata-aware: able to search, analyze, and reason through your catalog just like a seasoned data expert. Here’s what that looks like in action:

Search assets like a pro

Your agent can search across all metadata, by keyword, description, certification status, modification date, and more. It can filter by asset type, return specific fields, and sort or paginate results in seconds.

Try asking:

• “What customer data do we have that’s certified and updated weekly?”
• “Which tables feed into our marketing ROI model?”
• “Find all assets tagged with ‘churn’ modified in the last 7 days.”

Behind the scenes, the agent uses the search_assets tool to query Atlan’s catalog, saving time and delivering precise answers instantly.

Run advanced filters with DSL

For deeper queries, agents can use Atlan’s Domain Specific Language (DSL) to filter, score, and rank results. This is ideal for similarity searches, multi-condition filtering, or prioritizing results by business impact.

Try asking:

• “Which data assets are most similar to our churn model?”
• “Find all assets with names containing ‘revenue’ that are uncertified and used in more than one dashboard.”
• “What are the top 5 tables by query volume this month?”

These kinds of nuanced searches go beyond what’s possible in most catalogs—and give your AI assistant the power to surface the most relevant assets quickly.

Understand lineage and impact

With the traverse_lineage tool, your agent can trace upstream and downstream relationships across your data stack. It can explore dependencies, flag risk, and even simulate impact.

Try asking:

• “Show me every dashboard that depends on the customer_lifetime_value column.”
• “What downstream assets would be impacted if I updated the product_sku column?”
• “Compare lineage paths for our sales vs. returns KPIs.”

For example, a data engineer preparing to change a column could ask: “What dashboards or models depend on this field?” The agent returns a map of every affected downstream asset, helping prevent breakages and ensure smooth rollouts.

Monitor quality and trust at scale

Agents can be configured to run proactive checks, like scanning for missing descriptions, outdated certifications, or undocumented assets.

Try asking:

• “Which Snowflake tables are missing a description?”
• “Find all columns tagged as PII that haven’t been reviewed in the last 30 days.”
• “Which Looker dashboards are powered by uncertified data?”

Instead of waiting for quality issues to be flagged downstream, your agent surfaces them early, keeping your catalog clean and your data trustworthy.

How to Set Up Atlan MCP?

You can deploy the Atlan MCP Server with any AI client that supports MCP. Use the following configuration to get started:

{
  "mcpServers": {
    "atlan": {
      "command": "docker",
      "args": [
        "run",
        "-i",
        "--rm",
        "-e",
        "ATLAN_API_KEY=<YOUR_API_KEY>",
        "-e",
        "ATLAN_BASE_URL=https://<YOUR_INSTANCE>.atlan.com",
        "-e",
        "ATLAN_AGENT_ID=<YOUR_AGENT_ID>",
        "ghcr.io/atlanhq/atlan-mcp-server:latest"
      ]
    }
  }
}

For full setup instructions, see the Atlan MCP Server README on GitHub.

Why This Matters

The Atlan MCP Server brings structured, AI-ready access to your metadata. It allows AI agents to act with context and precision, whether they’re surfacing data assets, tracing lineage, or monitoring quality.

For teams building AI tools or enabling automation across the data stack, this opens up a new level of control and scalability. By making your metadata accessible through a standardized protocol, you reduce time spent on manual lookups and empower both technical and non-technical users to make better decisions, faster.

What’s Next

This launch is just the beginning. The Atlan MCP Server is a foundational step in our journey toward an AI-native future, where intelligent agents aren’t just bolted onto data workflows, but deeply embedded in how we discover, govern, and activate data.

We’re actively expanding the MCP Server with additional tools, deeper integrations, and broader support for the AI builder ecosystem. Our goal is to make it even easier for agents to work across your entire data stack, bringing metadata context to wherever your decisions are being made.

We’re inviting you to explore what’s possible. Whether you’re building an AI agent, experimenting with intelligent copilots, or embedding metadata into everyday decision-making, we’d love to partner with you on what comes next.

Your ideas will help shape this journey. Create a feature request directly in the GitHub repository. We can’t wait to see what you come up with.

Atlan MCP Resources

• Atlan MCP GitHub Repository
• Atlan API Authentication Guide
• Model Context Protocol Documentation

Get Support

For help getting set up or to share feedback, reach out to your Atlan Customer Success Manager (CSM) or Customer Solutions Architect (CSA).

For technical assistance along the way, we’re always ready to help at [email protected].