Azure Data Lake Storage MCP Server by CData

Enables access to ADP data through SQL queries, allowing retrieval of HR and payroll information via natural language questions.

Provides SQL-based access to Airtable data, allowing retrieval of information from bases and tables through natural language queries.

Enables querying of DynamoDB data using SQL, making NoSQL data accessible through natural language questions.

Allows querying data stored in Amazon S3 through a SQL interface, making it accessible via natural language queries.

Allows retrieval of task, project, and team data from Asana through SQL queries initiated by natural language questions.

Provides access to Basecamp project management data through SQL queries triggered by natural language questions.

Enables retrieval of BigCommerce store data including products, orders, and customers through SQL interfaces.

Allows querying of Bitbucket repositories, pull requests, and other data through SQL-based access.

Provides access to Box file storage data, enabling natural language queries about files and folders.

Enables retrieval of Confluence wiki content and metadata through SQL queries initiated by natural language questions.

Allows querying of Couchbase NoSQL database through a SQL interface, making data accessible via natural language.

Provides SQL-based access to Databricks data lakes and warehouses through natural language queries.

Enables retrieval of Dropbox file storage information through SQL queries triggered by natural language questions.

Allows querying of eBay marketplace data through SQL, making listing and sales information accessible via natural language.

Provides SQL-based access to Elasticsearch indices, enabling natural language querying of search data.

Enables querying of EnterpriseDB PostgreSQL databases through SQL interfaces initiated by natural language questions.

Allows retrieval of Facebook social media data through SQL queries triggered by natural language questions.

Provides access to GitHub repositories, issues, and other development data through SQL-based interfaces.

Enables querying of Gmail email data through SQL interfaces initiated by natural language questions.

Allows retrieval of Google Ads campaign and performance data through SQL queries.

Provides SQL-based access to Google Analytics website performance data through natural language queries.

Enables retrieval of Google Calendar event and schedule data through SQL interfaces initiated by natural language.

Allows querying of Google Campaign Manager 360 advertising data through SQL interfaces.

Provides access to Google Cloud Storage file data through SQL queries triggered by natural language questions.

Enables retrieval of Google Drive file storage information through SQL-based interfaces.

Allows querying of Google Sheets spreadsheet data through SQL interfaces initiated by natural language questions.

Provides SQL-based access to GraphQL APIs, enabling natural language querying of GraphQL data sources.

Allows retrieval of Greenhouse recruiting and applicant tracking data through SQL queries.

Enables querying of Apache Hive data warehouses through SQL interfaces initiated by natural language questions.

Provides access to HubSpot CRM, marketing, and sales data through SQL-based queries.

Allows retrieval of Instagram social media data through SQL queries triggered by natural language questions.

Enables querying of Jira issue tracking and project management data through SQL interfaces.

Allows retrieval of MailChimp email marketing campaign and subscriber data through SQL interfaces.

Enables querying of MariaDB databases through SQL interfaces initiated by natural language questions.

Provides SQL-based access to MongoDB NoSQL databases, making document data accessible via natural language.

Allows retrieval of MYOB AccountRight accounting data through SQL queries triggered by natural language questions.

Enables querying of MySQL databases through SQL interfaces initiated by natural language questions.

Provides SQL-based access to Neo4j graph databases, making graph data accessible via natural language queries.

Allows retrieval of Odoo ERP and business application data through SQL interfaces.

Enables querying of Okta identity management data through SQL queries initiated by natural language questions.

Provides access to PayPal payment and transaction data through SQL-based interfaces.

Allows retrieval of Pinterest social media data through SQL queries triggered by natural language questions.

Enables querying of PostgreSQL databases through SQL interfaces initiated by natural language questions.

Provides SQL-based access to Presto distributed query engine data sources through natural language queries.

Allows retrieval of QuickBooks accounting and financial data through SQL interfaces.

Enables querying of Redis key-value stores through SQL interfaces initiated by natural language questions.

Provides SQL-based access to RSS feed content, enabling natural language querying of feed data.

Allows retrieval of Sage accounting software data through SQL queries triggered by natural language questions.

Enables querying of Salesforce CRM data including contacts, opportunities, and accounts through SQL interfaces.

Provides access to SAP ERP and business application data through SQL-based interfaces.

Allows retrieval of SendGrid email delivery and campaign data through SQL queries.

Enables querying of Shopify e-commerce store data through SQL interfaces initiated by natural language questions.

Provides SQL-based access to SingleStore databases through natural language queries.

Allows retrieval of Slack messaging and workspace data through SQL queries triggered by natural language questions.

Enables querying of Snapchat Ads data through SQL interfaces initiated by natural language questions.

Provides access to Snowflake data warehouses through SQL-based interfaces and natural language queries.

Allows retrieval of Splunk log and machine data through SQL queries triggered by natural language questions.

Enables querying of Square payment and point-of-sale data through SQL interfaces.

Provides SQL-based access to Stripe payment processing data through natural language queries.

Allows retrieval of SurveyMonkey survey data and responses through SQL interfaces.

Enables querying of Teradata databases through SQL interfaces initiated by natural language questions.

Provides access to Trello board, list, and card data through SQL-based interfaces.

Allows retrieval of data through Trino distributed SQL query engine via natural language queries.

Enables querying of Twilio communications platform data through SQL interfaces.

Provides SQL-based access to WooCommerce store data including products, orders, and customers.

Allows retrieval of WordPress content management system data through SQL queries.

Enables querying of Xero accounting data through SQL interfaces initiated by natural language questions.

Provides SQL-based access to XML data files, enabling natural language querying of structured XML information.

Allows retrieval of YouTube Analytics data through SQL queries triggered by natural language questions.

Enables querying of Zendesk customer service and support ticket data through SQL interfaces.

Provides access to Zoho business application data including CRM, Books, and Inventory through SQL-based interfaces.

azure-data-lake-storage-mcp-server-by-cdata

CData's Model Context Protocol (MCP) Server for Azure Data Lake Storage

: This project builds a read-only MCP server. For full read, write, update, delete, and action capabilities and a simplified setup, check out our free CData MCP Server for Azure Data Lake Storage (beta).

Purpose

We created this read-only MCP Server to allow LLMs (like Claude Desktop) to query live data Azure Data Lake Storage supported by the CData JDBC Driver for Azure Data Lake Storage.

CData JDBC Driver connects to Azure Data Lake Storage by exposing them as relational SQL models.

This server wraps that driver and makes Azure Data Lake Storage data available through a simple MCP interface, so LLMs can retrieve live information by asking natural language questions — no SQL required.

Setup Guide

Clone the repository:
git clone https://github.com/cdatasoftware/azure-data-lake-storage-mcp-server-by-cdata.git cd azure-data-lake-storage-mcp-server-by-cdata
Build the server:
mvn clean install
This creates the JAR file: CDataMCP-jar-with-dependencies.jar
Download and install the CData JDBC Driver for {source}: https://www.cdata.com/drivers/azuredatalake/download/jdbc
License the CData JDBC Driver:
- Navigate to the lib folder in the installation directory, typically:
  - (Windows) C:\Program Files\CData\CData JDBC Driver for Azure Data Lake Storage\
  - (Mac/Linux) /Applications/CData JDBC Driver for Azure Data Lake Storage/
- Run the command java -jar cdata.jdbc.adls.jar --license
- Enter your name, email, and "TRIAL" (or your license key).
Configure your connection to the data source (Salesforce as an example):
- Run the command java -jar cdata.jdbc.adls.jar to open the Connection String utility.
- Configure the connection string and click "Test Connection"
  Note: If the data sources uses OAuth, you will need to authenticate in your browser.
- Once successful, copy the connection string for use later.
Create a .prp file for your JDBC connection (e.g. azure-data-lake-storage.prp) using the following properties and format:
- Prefix - a prefix to be used for the tools exposed
- ServerName - a name for your server
- ServerVersion - a version for your server
- DriverPath - the full path to the JAR file for your JDBC driver
- DriverClass - the name of the JDBC Driver Class (e.g. cdata.jdbc.adls.ADLSDriver)
- JdbcUrl - the JDBC connection string to use with the CData JDBC Driver to connect to your data (copied from above)
- Tables - leave blank to access all data, otherwise you can explicitly declare the tables you wish to create access for
  Prefix=adls ServerName=CDataADLS ServerVersion=1.0 DriverPath=PATH\TO\cdata.jdbc.adls.jar DriverClass=cdata.jdbc.adls.ADLSDriver JdbcUrl=jdbc:adls:InitiateOAuth=GETANDREFRESH; Tables=

Using the Server with Claude Desktop

Create the config file for Claude Desktop ( claude_desktop_config.json) to add the new MCP server, using the format below. If the file already exists, add the entry to the mcpServers in the config file. Windows
{ "mcpServers": { "{classname_dash}": { "command": "PATH\\TO\\java.exe", "args": [ "-jar", "PATH\\TO\\CDataMCP-jar-with-dependencies.jar", "PATH\\TO\\azure-data-lake-storage.prp" ] }, ... } }
Linux/Mac
{ "mcpServers": { "{classname_dash}": { "command": "/PATH/TO/java", "args": [ "-jar", "/PATH/TO/CDataMCP-jar-with-dependencies.jar", "/PATH/TO/azure-data-lake-storage.prp" ] }, ... } }
If needed, copy the config file to the appropriate directory (Claude Desktop as the example). Windows
cp C:\PATH\TO\claude_desktop_config.json %APPDATA%\Claude\claude_desktop_config.json
Linux/Mac
cp /PATH/TO/claude_desktop_config.json /Users/{user}/Library/Application\ Support/Claude/claude_desktop_config.json'
Run or refresh your client (Claude Desktop).

Note: You may need to fully exit or quit your Claude Desktop client and re-open it for the MCP Servers to appear.

Running the Server

Run the follow the command to run the MCP Server on its own
java -jar /PATH/TO/CDataMCP-jar-with-dependencies.jar /PATH/TO/Salesforce.prp > **Note:** The server uses `stdio` so can only be used with clients that run on the same machine as the server.

Usage Details

Once the MCP Server is configured, the AI client will be able to use the built-in tools to read, write, update, and delete the underlying data. In general, you do not need to call the tools explicitly. Simply ask the client to answer questions about the underlying data system. For example:

"What is the correlation between my closed won opportunities and the account industry?"
"How many open tickets do I have in the SUPPORT project?"
"Can you tell me what calendar events I have today?"

The list of tools available and their descriptions follow:

Tools & Descriptions

In the definitions below, {servername} refers to the name of the MCP Server in the config file (e.g. {classname_dash} above).

{servername}_get_tables - Retrieves a list of tables available in the data source. Use the {servername}_get_columns tool to list available columns on a table. The output of the tool will be returned in CSV format, with the first line containing column headers.
{servername}_get_columns - Retrieves a list of columns for a table. Use the {servername}_get_tables tool to get a list of available tables. The output of the tool will be returned in CSV format, with the first line containing column headers.
{servername}_run_query - Execute a SQL SELECT query

JSON-RPC Request Examples

If you are scripting out the requests sent to the MCP Server instead of using an AI Client (e.g. Claude), then you can refer to the JSON payload examples below – following the JSON-RPC 2.0 specification - when calling the available tools.

azure_data_lake_storage_get_tables

{
    "jsonrpc": "2.0",
    "id": 1,
    "method": "tools/call",
    "params": {
        "name": "azure_data_lake_storage_get_tables",
        "arguments": {}
    }
}

azure_data_lake_storage_get_columns

{
    "jsonrpc": "2.0",
    "id": 2,
    "method": "tools/call",
    "params": {
        "name": "azure_data_lake_storage_get_columns",
        "arguments": {
            "table":  "Account"
        }
    }
}

azure_data_lake_storage_run_query

{
    "jsonrpc": "2.0",
    "id": 3,
    "method": "tools/call",
    "params": {
        "name": "azure_data_lake_storage_run_query",
        "arguments": {
            "sql":  "SELECT * FROM [Account] WHERE [IsDeleted] = true"
        }
    }
}

Troubleshooting

If you cannot see your CData MCP Server in Claude Desktop, be sure that you have fully quit Claude Desktop (Windows: use the Task Manager, Mac: use the Activity Monitor)
If Claude Desktop is unable to retrieve data, be sure that you have configured your connection properly. Use the Connection String builder to create the connection string (see above) and copy the connection string into the property (.prp) file.
If you are having trouble connecting to your data source, contact the CData Support Team.
If you are having trouble using the MCP server, or have any other feedback, join the CData Community.

License

This MCP server is licensed under the MIT License. This means you are free to use, modify, and distribute the software, subject to the terms and conditions of the MIT License. For more details, please see the LICENSE file in the project repository.

All Supported Sources

This server cannot be installed

security - not tested

license - permissive license

quality - not tested

How are these scores calculated?

local-only server

The server can only run on the client's local machine because it depends on local resources.

Azure Data Lake Storage MCP Server by CData

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Azure Data Lake Storage MCP Server by CData

azure-data-lake-storage-mcp-server-by-cdata

Purpose

Setup Guide

Using the Server with Claude Desktop

Running the Server

Usage Details

Tools & Descriptions

JSON-RPC Request Examples

azure_data_lake_storage_get_tables

azure_data_lake_storage_get_columns

azure_data_lake_storage_run_query

Troubleshooting

License

All Supported Sources

Related MCP Servers

Microsoft Dataverse MCP Server by CData

Azure Active Directory MCP Server by CData

Azure Synapse MCP Server by CData

Azure Table MCP Server by CData

New MCP Servers

MCP directory API