Getting Started Checklist
==============================

If you are new to the Dimensions Search Language and the Dimensions Analytics API, here is a list of useful things to get you started.

[1] Verify your connection.
    Ensure you can access the API using your personal credentials, via one of the methods described in the :ref:`api section <api-access>`.

[2] Install the open source Dimcli library.
    `Dimcli <https://github.com/lambdamusic/dimcli>`_ is a command-line tool and Python library that allows to create DSL queries interactively via an autocomplete mechanism, hence making it easier to learn the language and discover new features. 

[3] Do the tutorial. 
    The :ref:`Tour of the DSL <tour>` section introduces the most important DSL topics gradually and with straightforward examples, so it is a great way to get a feeling for what the API has to offer. 

[4] Explore the data sources model.
    The :ref:`Data Sources <data-sources>` section includes detailed information about all the main record types available to query, their properties and cross-links.  

[5] Try out real-world applications.
    The `Dimensions API Lab <https://digital-science.github.io/dimensions-api-lab/>`_  is an open source repository of Jupyter notebooks demonstrating how to use the DSL to carry out different scholarly analytics tasks, e.g. building a citation network, doing a journals competitors analysis, tracking researchers' affiliations over time etc..

[6] Connect an AI assistant via MCP.
    The :ref:`Dimensions Analytics MCP <mcp>` server lets you search and analyze Dimensions data through natural language in Cursor, Claude Desktop, VS Code, and other MCP-compatible clients.