Load Glama MCP data in Python using dltHub

Build a Glama MCP-to-database or-dataframe pipeline in Python using dlt with automatic Cursor support.

In this guide, we'll set up a complete Glama MCP data pipeline from API credentials to your first data load in just 10 minutes. You'll end up with a fully declarative Python pipeline based on dlt's REST API connector, like in the partial example code below:

Example code
@dlt.source
def glama_mcp_source(access_token=dlt.secrets.value):
    config: RESTAPIConfig = {
        "client": {
            "base_url": "https://glama.ai/api/mcp/v1/servers",
            "auth": {
                "type": "bearer",
                "token": access_token,
            }
        },
        "resources": [
            "datastax", "ConnorBoetig-dev"
        ],
    }
    [...]
    yield from rest_api_resources(config)


def get_data() -> None:
    # Connect to destination
    pipeline = dlt.pipeline(
        pipeline_name='glama_mcp_pipeline',
        destination='duckdb',
        dataset_name='glama_mcp_data', 
    )
    # Load the data
    load_info = pipeline.run(glama_mcp_source())
    print(load_info)

Why use dltHub Workspace with LLM Context to generate Python pipelines?

  • Accelerate pipeline development with AI-native context
  • Debug pipelines, validate schemas and data with the integrated Pipeline Dashboard
  • Build Python notebooks for end users of your data
  • Low maintenance thanks to Schema evolution with type inference, resilience and self documenting REST API connectors. A shallow learning curve makes the pipeline easy to extend by any team member
  • dlt is the tool of choice for Pythonic Iceberg Lakehouses, bringing mature data loading to pythonic Iceberg with or without catalogs

What you’ll do

We’ll show you how to generate a readable and easily maintainable Python script that fetches data from glama_mcp’s API and loads it into Iceberg, DataFrames, files, or a database of your choice. Here are some of the endpoints you can load:

  • Mobile MCP Server: Endpoint for mobile-related MCP operations, specifically for the mobile-next server.
  • 302 AI Browser Use MCP: Endpoint for accessing MCP features related to the 302 AI server and its browser usage.
  • Warpdev MCP Hub: Endpoint for the development environment of the MCP hub on the warpdev server.
  • Connor Boetig Dev MCP: Endpoint for MCP functionalities on the Connor Boetig development server.
  • Astra DB MCP: Endpoint for interacting with the Astra database through the MCP on the Datastax server.

You will then debug the Glama MCP pipeline using our Pipeline Dashboard tool to ensure it is copying the data correctly, before building a Notebook to explore your data and build reports.

Setup & steps to follow

💡
Before getting started, let's make sure Cursor is set up correctly:

Now you're ready to get started!

  1. ⚙️ Set up dlt Workspace

    Install dlt with duckdb support:

    pip install "dlt[workspace]"

    Initialize a dlt pipeline with Glama MCP support.

    dlt init dlthub:glama_mcp duckdb

    The init command will setup the necessary files and folders for the next step.

  2. 🤠 Start LLM-assisted coding

    Here’s a prompt to get you started:

    Prompt
    Please generate a REST API Source for Glama MCP API, as specified in @glama_mcp-docs.yaml 
Start with endpoints datastax and ConnorBoetig-dev and skip incremental loading for now. 
Place the code in glama_mcp_pipeline.py and name the pipeline glama_mcp_pipeline. 
If the file exists, use it as a starting point. 
Do not add or modify any other files. 
Use @dlt rest api as a tutorial. 
After adding the endpoints, allow the user to run the pipeline with python glama_mcp_pipeline.py and await further instructions.

  3. 🔒 Set up credentials

    A Bearer Token is required for authentication, which can be obtained from the specified source and should be applied in the authorization header.

    To get the appropriate API keys, please visit the original source at https://glama.ai/mcp/servers/@gujord/OpenAPI-MCP/blob/main/test/fixtures/invalid.yaml. If you want to protect your environment secrets in a production environment, look into setting up credentials with dlt.

  4. 🏃‍♀️ Run the pipeline in the Python terminal in Cursor

    python glama_mcp_pipeline.py

    If your pipeline runs correctly, you’ll see something like the following:

    Pipeline glama_mcp load step completed in 0.26 seconds
1 load package(s) were loaded to destination duckdb and into dataset glama_mcp_data
The duckdb destination used duckdb:/glama_mcp.duckdb location to store data
Load package 1749667187.541553 is LOADED and contains no failed jobs

  5. 📈 Debug your pipeline and data with the Pipeline Dashboard

    Now that you have a running pipeline, you need to make sure it’s correct, so you do not introduce silent failures like misconfigured pagination or incremental loading errors. By launching the dlt Workspace Pipeline Dashboard, you can see various information about the pipeline to enable you to test it. Here you can see:

    • Pipeline overview: State, load metrics
    • Data’s schema: tables, columns, types, hints
    • You can query the data itself
    dlt pipeline glama_mcp_pipeline show

  6. 🐍 Build a Notebook with data explorations and reports

    With the pipeline and data partially validated, you can continue with custom data explorations and reports. To get started, paste the snippet below into a new marimo Notebook and ask your LLM to go from there. Jupyter Notebooks and regular Python scripts are supported as well.

    import dlt

data = dlt.pipeline("glama_mcp_pipeline").dataset()
# get "datastax" table as Pandas frame
data."datastax".df().head()

Extra resources:

Next steps