data load tool (dlt) — MCP Server

🚀 Follow this guide to create a dlt pipeline in 10mins with AI

How is it useful?

Large language models (LLMs) know a lot about the world, but nothing about your specific code and data.

The Model Context Protocol (MCP) server allows the LLM to retrieve up-to-date and correct information about your dlt pipelines, datasets, schema, etc. This significantly improves the development experience in AI-enabled IDEs (Copilot, Cursor, Continue, Claude Code, etc.)

Installation

The package manager uv is required to launch the MCP server.

Add this section to your MCP configuration file inside your IDE.

{
  "name": "dlt",
  "command": "uv",
  "args": [
    "run",
    "--with",
    "dlt-mcp[search]",
    "python",
    "-m",
    "dlt_mcp"
  ],
}

Note

The configuration file format varies slightly across IDEs

Features

Tools

The dlt MCP server provides tools that allows the LLM to take actions:

• list_pipelines: Lists all available dlt pipelines. Each pipeline consists of several tables.
• list_tables: Retrieves a list of all tables in the specified pipeline.
• get_table_schemas: Returns the schema of the specified tables.
• execute_sql_query: Executes a SELECT SQL statement for simple data analysis.
• get_load_table: Retrieves metadata about data loaded with dlt.
• get_pipeline_local_state: Fetches the state information of the pipeline, including incremental dates, resource state, and source state.
• get_table_schema_diff: Compares the current schema of a table with another version and provides a diff.
• search_docs: Searches over the dlt documentation using different modes (hybrid, full_text, or vector) to verify features and identify recommended patterns.
• search_code: Searches the source code for the specified query and optional file path, providing insights into internal code structures and patterns.