Build Your AI Context Stack
Get the blueprint for implementing context graphs across your enterprise. This guide walks through the four-layer architecture from metadata foundation to agent orchestration, with practical implementation steps for 2026.
Get the Stack GuideWhy does the dbt MCP server matter?
Business semantics are captured and refined throughout the ETL or ELT process, with data transformation as a key stage. As the dbt Semantic Layer documentation makes clear, most of the business logic that drives reporting, analysis, and AI-readable metrics resides in the transformation layer.
MCP is one of several ways dbt exposes that logic; it also serves Semantic Layer metrics over JDBC, ADBC, GraphQL and a Python SDK. What MCP adds is a delivery surface an agent can call natively. Three things make the dbt MCP server meaningful for AI teams:
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Business logic centralization: MCP server makes business definitions agent-readable without extra documentation.
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Metric governance at query time: MetricFlow enforces consistent metric definitions at the point of query, and so, agents get the same metric every time.
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Lineage as context: Data lineage captured through dbt runs tells agents about the origins and dependencies of each metric.
What does the dbt MCP server involve?
The dbt MCP server sits between the dbt Semantic Layer and any MCP-compatible agent or tool. Three components work together to make this possible.
The Model Context Protocol (MCP)
MCP is an open standard, originally introduced by Anthropic, that defines how AI agents discover and consume context from external systems at inference time. It standardizes the interface between agent reasoning layers and external knowledge sources, so any MCP-compatible agent can query any MCP-compatible server using the same protocol.
The dbt Semantic Layer
The dbt Semantic Layer is dbt’s framework for defining metrics, dimensions, and entities separately from the underlying data models. It centralizes business definitions so they can be consumed consistently across BI tools, AI agents, and other downstream systems.
MetricFlow
MetricFlow is the metric definition layer within dbt. Data teams use it to specify how metrics are calculated, which dimensions they can be sliced by, which entities they belong to, and which aggregations are valid.
What can you do with the MCP server for dbt?
The dbt MCP server allows agents and users to access various tools that dbt exposes. dbt’s documentation lists nine toolsets: dbt platform CLI commands, Semantic Layer, SQL, Metadata Discovery, Administrative API, Codegen, dbt v2 Tools, Product Docs, and MCP Server Metadata. One of them gives external agents access to the dbt Semantic Layer using the following six tools:
list_metrics: Allows agents to retrieve all the metrics defined in your dbt project.get_dimensions: Lets agents fetch the dimensions for you to slice a particular metric.get_entities: Lets agents get the entities (join keys) available for a particular metric.query_metrics: Allows agents to execute queries based on metric definitions.list_saved_queries: Lets agents fetch all the saved queries.get_metrics_compiled_sql: Allows agents to get a compiled SQL query for a metric using MetricFlow.
Two toolsets work closely with the Semantic Layer toolset:
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SQL tools: Helpful in natural language to SQL query translation.
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Metadata Discovery tools: Provide context to the Semantic Layer tools.
How can you set up the dbt MCP server for agents?
Before setting up the dbt MCP server, ensure you have a dbt platform account and a dbt project deployed in a production environment.
The dbt platform Account Admin will need to create credentials in the form of a Personal Access Token (PAT), or more likely, a Service Token with the right permission to call Semantic Layer tools, Metadata Discovery tools, and the Developer APIs.
To interact with the MCP server, the agent will need to route through an MCP client. Here are the steps to create this MCP server:
- Define metrics on top of your semantic models.
- Enable the Semantic Layer and connect it to the data warehouse.
- Figure out authentication and authorization (PAT/Service Token).
- Get the connection details with the
DBT_HOSTvalue. - Get the production environment ID from the dbt platform.
- Use a remote MCP endpoint (local is also possible for some use cases).
- Integrate the dbt MCP server with your MCP client.
Once you connect to the dbt MCP server, any AI agents you have will be able to reap the benefits of the toolsets that the server exposes. The dbt MCP server delivers what dbt models. Atlan carries ownership, lineage and validation for metrics defined in dbt and in every other tool a team runs, which is the layer above it.
CIO Guide to Context Graphs
For data leaders evaluating where to start, Atlan's CIO guide to context graphs walks through a practical four-layer architecture from metadata foundation to agent orchestration.
Get the CIO GuideWhy is the dbt MCP server alone not enough?
Like many tools in the data stack, dbt is limited by its own surface area and what it controls. One of the key benefits of the dbt MCP server is that it exposes the various types of metadata captured and curated in dbt.
However, dbt’s metadata, semantics, and hence, context stop at the dbt boundary. Lineage, governance, semantics, and business metadata don’t flow losslessly across tools.
That’s where a context layer is needed: one that can integrate with MCP servers and the APIs of a wide variety of tools, and provide a unified context for all the other tools in the data stack to consume.
Atlan provides the precise context layer you need to get the most out of an agentic data stack. For instance, teams using Atlan’s Context Engineering Studio can complement the dbt MCP server context with governance context, ownership metadata, and policy signals on top of the dbt metric definitions.
How does Atlan extend the dbt semantic layer for AI agents?
Atlan has been involved in the evolution of the dbt Semantic Layer, as it was one of the few launch partners of the dbt Semantic Layer back in 2022. Since then, AI agents have become mainstream, and so have agentic data stacks and MCP servers.
The following features of Atlan are centered on providing better integration with dbt to get the most out of not just the Semantic Layer but also SQL tools, Metadata Discovery, among others:
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Organization-wide metadata collection: Atlan gathers metadata across your full data estate, and turns it into real, viable, and useful context that agents can query.
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AI-native knowledge graph: An AI-native knowledge graph that connects all assets, glossary, metrics, and reports into a unified context layer for agents.
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Cross-system, column-level lineage: Fine-grained data lineage tracks data flow across the various ingestion, transformation, and BI tools in your data stack.
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Common vocabulary and ontology: A shared business glossary and ontology covers metrics and reports beyond what MetricFlow alone can support.
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Trust signals in datasets and metrics: Atlan embeds trust signals in datasets and metrics to give quality and reliable context to agents.
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Bi-directional sync with dbt: Atlan offers a bi-directional sync with dbt to sync tags, owners, lineage, descriptions, classifications, glossary, etc. Changes in either system propagate without manual reconciliation.
Many of the features that make up the context layer are rooted in Atlan’s data governance capabilities, for which it has been recognized as a Leader in the 2026 Gartner Magic Quadrant for Data & Analytics Governance Platforms.
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Download EbookMoving forward with MCP server for dbt
In the ETL/ELT processes, the most significant is the T, the transformation process, which dbt addresses. The transformation code contains the most important business logic, rules, and conditions for your organization’s data.
This transformation logic is absolutely crucial for ensuring accurate metrics, reports, and dashboards, and that data is available to users via APIs and the dbt MCP server. The MCP server allows AI agents to get access to the metadata via various toolsets for the Semantic Layer, Metadata Discovery, SQL, among other things.
What is still needed is the context held outside dbt: definitions written in other tools, the owners accountable for them, and whether each one is still certified. That’s where Atlan comes in.
Atlan’s context layer is an AI knowledge graph-powered accumulation and organization of lineage, governance, metrics, and quality metadata across your data stack. It turns fragmented, siloed metadata into a single, unified context layer available to any and every AI agent in your data and AI stack.
FAQs about MCP server for dbt
1. How does the dbt MCP server work?
The dbt MCP server exposes dbt-specific tools for the Semantic Layer, Metadata Discovery, and SQL tools. These tools are helpful both for agentic workflows in other tools and also for human users developing dbt code directly on the dbt platform or using platforms like Databricks and Snowflake.
2. What permissions are required for agents to work with the dbt MCP server?
When you’ve configured agents to use Service Tokens, three permissions are required: Semantic Layer Only, Metadata Only, and Developer, whereas when you use PATs, the token inherits the requesting user’s permissions. When you set up the Semantic Layer initially, you will need Account Admin permissions.
3. What’s the difference between a local and a remote dbt MCP server?
The remote dbt MCP server is hosted at a dbt Labs endpoint and is available on all dbt platform plans; the Codegen and CLI toolsets are self-hosted only. It’s the type of MCP server you need to integrate with agents for external tools for BI, governance, lineage, cataloging, and analytics. The local dbt MCP server is built more for local testing and development.
4. Can the dbt MCP server work with the Atlan MCP?
Yes, dbt MCP server exposes the Semantic Layer, Metadata Discovery, and other toolsets for the Atlan MCP server to access. Atlan’s MCP server can retrieve relevant metadata for context and metric definitions, enabling search, discovery, cataloging, lineage, and governance, among other things.
5. Does the dbt MCP server for the Semantic Layer work with dbt OSS?
No. To use the Semantic Layer and its toolset you need a dbt platform account with a production environment, on a Starter or Enterprise tier. The self-hosted server does support local projects with or without a dbt platform account, and it carries the dbt CLI and Codegen tools. On naming: dbt v2 went GA on 16 September 2026, and what was dbt Core v2 is now dbt OSS, the Apache 2.0 distribution.
