# Nasdaq Data Link MCP > Nasdaq Data Link (Quandl) connects your AI agent to professional-grade financial and economic datasets. Use natural language to query datatables, check dataset schemas for metadata, or trigger massive bulk downloads from top vendors like the S&P 500 index providers. It lets you pull complex financial history without writing a single API script. ## Overview - **Category:** money-moves - **Price:** Free - **Tags:** financial-data, stock-market, economic-indicators, quandl, nasdaq ## Description This MCP gives your agent direct access to professional-grade data sets covering finance and economics. Instead of spending hours figuring out which vendor table holds the fundamental data you need, you just ask for it. Your agent handles the connection and pulls unsorted records using specific filters like tickers or date ranges. Need to know what columns are even available before running a query? You can check the metadata first. For massive analysis, you don't have to handle pagination manually; simply request bulk downloads for entire datasets in formats like CSV or Parquet. This capability means you get raw data into your environment fast, letting you focus on modeling instead of data plumbing. All 4,000+ MCPs are available through Vinkius, making this a single place to access global financial intelligence. ## Tools ### request_bulk_download Starts an export process for an entire dataset and reports the status (PENDING, RUNNING, SUCCEEDED) along with file URLs. ### get_bulk_download_file Downloads a specific bulk file once the data export has been successfully processed. ### get_datatable Pulls unsorted, filtered data points from a specified Nasdaq datatable using defined criteria like tickers and dates. ### get_datatable_metadata Retrieves the full description of a datatable, listing its column types and identifying which columns can be used as filters. ## Prompt Examples **Prompt:** ``` Show me the metadata for the MER/F1 datatable. ``` **Response:** ``` I've retrieved the metadata for MER/F1. This table contains fundamental financial data. Available columns include 'reportid', 'unitid', 'indicatorid', and 'value'. The columns 'ticker' and 'date' are primary filters. ``` **Prompt:** ``` Fetch the latest 5 records from WIKI/PRICES for ticker MSFT. ``` **Response:** ``` Querying WIKI/PRICES for MSFT... I found the most recent data points. On 2018-03-27, the close price was 89.47. Would you like me to export the full history for this ticker? ``` **Prompt:** ``` Request a bulk download for the ZACKS/CP datatable. ``` **Response:** ``` I have initiated the bulk download request for ZACKS/CP. The current status is 'PENDING'. I will monitor the export and let you know when the file is ready for download. ``` ## Capabilities ### Query specific datasets Tell your agent which datatable and filters you need, and it pulls the unsorted data directly. ### Inspect dataset structure Check a table's metadata to see exactly what columns are available and how they should be filtered before querying. ### Initiate large exports Request the system start generating massive files for entire datasets, receiving status updates like PENDING or RUNNING. ### Download processed files After a bulk export finishes, you can download the final data file in CSV, Parquet, or ZIP format. ## Use Cases ### Building a Quarterly Performance Report A Financial Analyst needs Q3 data for 10 key stocks. Instead of running 10 separate API calls, they ask the agent to query datatables with advanced filtering for the specific quarter and list of tickers, getting all unsorted results in one go. ### Modeling a Market Indicator Shift A Quantitative Researcher wants to model how bond yields correlate with energy prices. They first use `get_datatable_metadata` to understand the available columns, then use `request_bulk_download` on both data sets for a decade's worth of records. ### Investigating an Outlier Stock Price A Data Scientist finds a strange price movement. They ask the agent to fetch the last 20 records from that stock’s datatable, instantly seeing the raw data points and confirming if the pattern is real or an error. ### Preparing for Deep Research A user needs a massive dataset of fundamental company metrics. They tell the agent to start the bulk download for the entire ZACKS/CP datatable, setting it up and getting status updates while they work on other tasks. ## Benefits - Stop writing complex API scripts. You can ask your agent to get unsorted data using specific filters (like tickers or dates) with the `get_datatable` tool, getting the numbers you need in seconds. - Before running a query, check the structure first. Use `get_datatable_metadata` to see exactly what columns exist and which ones can be filtered, preventing frustrating errors down the line. - Handling massive data sets is easy. Initiate huge exports for entire datasets using `request_bulk_download`. The agent tracks the status until you're ready to pull it with `get_bulk_download_file`. - The MCP handles large result set pagination and exporting, so you don’t have to worry about manually chunking data or dealing with API limits. Just tell your agent what you need. - You get access to professional-grade economic indicators and alternative financial metrics—data that is usually locked behind complex vendor portals. ## How It Works The bottom line is that it lets you access complex financial history using simple conversation instead of custom code. 1. Subscribe to this MCP and enter your Nasdaq Data Link API Key into your preferred AI client. 2. Use natural language prompts (e.g., 'Get the metadata for XYZ datatable') to let your agent inspect available datasets and required filters. 3. Execute a query or request a bulk download, and your agent delivers the raw data points or monitors the export status until you can download the final file. ## Frequently Asked Questions **How do I check which columns are available in a Nasdaq datatable?** Use the `get_datatable_metadata` tool. This tells you the table's description, its column types, and confirms exactly which fields can be used for filtering before you run a full query. **Can I download huge amounts of financial data with Nasdaq Data Link (Quandl)?** Yes. If the dataset is too large to fetch directly, use `request_bulk_download` first. The system handles the heavy lifting in the background and lets you pull the final file using `get_bulk_download_file`. **What if I only need a few rows of data?** Use the `get_datatable` tool. Just provide the datatable name, your desired filters (like date range and ticker), and it will pull the unsorted results directly to your agent. **Does this MCP work with all financial data?** It accesses datasets from various vendors available through Nasdaq Data Link. You must specify the exact datatable name when making a query or metadata request.