BEA MCP. Pull verified US economic data instantly.
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The BEA MCP lets you pull raw, verified US economic statistics like GDP and personal income directly from the Bureau of Economic Analysis.
Instead of fighting complex government APIs or downloading messy CSVs, your agent can browse all available datasets and retrieve precise historical data points right into your workflow.
What your AI agents can do
Get dataset list
Shows you a list of every major type of economic data set available (like NIPA or Regional).
Get data
Pulls specific raw economic figures from a BEA dataset after you define all the needed parameters.
Get parameter list
Lists all possible filters and variables you can apply to a specific dataset.
It shows you every major economic dataset available, like National Income and Product Accounts (NIPA) or Regional data.
You can ask it what specific filters or variables are needed for any given dataset before attempting a query.
It returns lists of valid codes for parameters, so you never submit an API request with an incorrect value.
Your agent fetches the specific economic data points—like real GDP growth—and feeds them back to your chat or script.
Ask AI about this MCP
Supported MCP Clients
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BEA MCP: BEA (Bureau of Economic Analysis) with 4 tools
These four tools let your agent systematically discover, validate, and retrieve specific economic data points from the Bureau of Economic Analysis.
Make your AI actually useful.
Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using BEA (Bureau of Economic Analysis) on Vinkius019e386cget dataset list
Shows you a list of every major type of economic data set available (like NIPA or Regional).
019e386cget data
Pulls specific raw economic figures from a BEA dataset after you define all the needed parameters.
019e386cget parameter list
Lists all possible filters and variables you can apply to a specific dataset.
019e386cget parameter values
Provides the exact, valid codes for parameters (like Annual or Quarterly) so your query won't fail.
Choose How to Get Started
Build a custom MCP for your own tools, or connect a ready-made integration from our catalog.
Build Your Own
Turn any API into an MCP. Import a spec, define Agent Skills, or deploy with MCPFusion.
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Make Your AI Do More
Start with BEA (Bureau of Economic Analysis), then connect any of our 5,000+ other servers whenever your AI needs more. One click, no limits.
- Use this MCP plus 5,000+ others, all in one place
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- Works with Claude, ChatGPT, Cursor, and more
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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by BEA (Bureau of Economic Analysis). All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This server provides 4 capabilities that interface natively with Claude, ChatGPT, Cursor, and any MCP client. No middleware. No custom integration required.
Manually gathering macroeconomics data is a nightmare.
Today, pulling key economic metrics means navigating multiple government websites. You find the right dataset page, figure out which table ID you need, and then cross-reference API documentation just to see if 'Annual' is coded as A or AN. Then, it’s copy-paste hell: dumping data into a spreadsheet and spending hours cleaning up missing values and formatting dates.
With this MCP, the process changes completely. You ask your agent what you need—say, real GDP for 2023. The tool handles the entire lookup chain in conversation. You get clean, usable numbers handed back instantly, ready to drop into a report or model.
Get accurate US economic figures using BEA MCP.
The most tedious steps that vanish are the cross-checks. You don't have to manually check API docs for valid codes; `get_parameter_values` handles that validation automatically. You also skip the time spent searching through dozens of pages just trying to find a dataset ID, because `get_dataset_list` points you right where you need to go.
It’s not just about getting data faster; it's about eliminating the risk of human error at every stage of the process. The data integrity is locked down.
What you can do with this MCP connector
You need to track how the U.S. economy performs—from quarterly growth rates to industrial output figures. This MCP connects you straight to the Bureau of Economic Analysis’s core data sources. You just tell your agent what period, what industry, and what metric you care about. It handles finding the right dataset, figuring out which variables are needed, and pulling the raw numbers.
The whole process happens conversationally. If you're using Vinkius to manage your MCP catalog, this connection gives you one reliable place for all major US macro data. You stop writing boilerplate code just to validate a table ID or figure out if 'Annual' is coded as 'A' or something else.
It’s pure data access, without the headache.
019e386c-aa44-73a7-b598-e208797f166d 
How BEA MCP Works
- 1 First, subscribe to this MCP and provide your BEA API Key. This gets your agent authenticated with the necessary access credentials.
- 2 Next, ask your agent to use
get_dataset_listorget_parameter_list. The tool talks back, giving you a structured list of options for you to narrow down the search scope. - 3 Finally, once the scope is clear, call
get_data, providing all the validated parameters. You get clean, raw economic figures returned directly into your conversation.
The bottom line is that it takes a messy, multi-step API process and reduces it to simple, guided chat commands.
Who Is BEA MCP For?
This MCP is for anyone who needs verifiable, historical US economic data but hates navigating complex government APIs. If you're an analyst who spends half a day just debugging why your script failed because of a bad parameter value, this is for you.
They need to pull historical GDP or regional income data quickly so they can feed macro trends into market forecasts without waiting on an internal data science team.
They use it daily to compare current inflation rates against long-term datasets like NIPA, automating the process of gathering figures that used to require manual spreadsheet work.
They pull government-verified economic signals for machine learning models, using the tool's parameter validation features to ensure data integrity in their pipelines.
What Changes When You Connect
- Skip the manual CSV download. You can ask for historical GDP or income figures and get them right in your conversation using
get_data. - Never guess a parameter value again. The
get_parameter_valuestool confirms valid codes (like frequency: 'A' or 'Q') before you run the query, eliminating API errors. - Quickly scope out data options by calling
get_dataset_list. You get an overview of NIPA, Regional, and other datasets without browsing multiple websites. - Build better models because you can automate the ingestion of government-verified economic signals. Use these figures in your analysis tool instead of relying on estimates.
- The tools let you walk through a structured process: first find available parameters with
get_parameter_list, then check valid values, and finally pull the data.
Real-World Use Cases
Comparing 2019 vs. 2023 GDP
A financial analyst needs to see how real gross domestic product changed between two years. They use get_dataset_list to confirm the right table, then run get_parameter_values to ensure they use 'Year' and 'Frequency' correctly before calling get_data for a direct comparison.
Investigating Regional Income Shifts
A researcher wants all available datasets related to regional income. They ask the agent, which uses get_dataset_list, and then use get_parameter_list on the National Income data to find specific variables needed for their report.
Validating Quarterly Data Structure
A data scientist is building a pipeline that needs quarterly (Q) figures. They call get_parameter_values to confirm 'Q' is the right code, ensuring their entire ML project uses consistent and validated input parameters.
Finding all available metrics
A student just wants a broad view of what BEA has. They start with get_dataset_list to see every category—NIPA, Fixed Assets, etc.—giving them a starting point for research.
The Tradeoffs
Calling data without validation
Trying to use get_data with a random parameter or value they just guessed. This will fail because the API rejects invalid inputs, stopping your script dead.
→
Always check first. Use get_parameter_list to see what filters are available for that dataset, and then run get_parameter_values to get the exact code you need before finally running get_data.
Assuming data scope
Writing a script that assumes all economic metrics fit under one single table. You won't know which tables apply to different types of data (e.g., regional vs. national).
→
Start by calling get_dataset_list. This tool shows you the primary dataset categories, keeping your queries focused and manageable.
Over-fetching or missing details
Requesting a massive block of data without specifying the year range or frequency. You'll either get too much noise or miss critical time periods.
→
Use get_parameter_list to identify all required variables (like 'Year' and 'Frequency'). Then, use get_parameter_values to define those boundaries precisely before calling get_data.
When It Fits, When It Doesn't
You should use this MCP if your core need is accessing verifiable US economic history. The key difference is structure: If you only need a quick list of datasets, just run get_dataset_list. But if you actually want to run an analysis or build a reliable script, the process must be layered. You have to validate first. Never call get_data without first confirming the necessary parameters using get_parameter_list, and then validating those parameters with get_parameter_values. If your job is purely data visualization based on pre-cleaned files, you don't need this MCP; stick with your current database connection instead.
Common Questions About BEA MCP
How do I start using get_dataset_list with BEA MCP? +
You simply ask your agent to list all available datasets. It will return a structured list of major categories, like NIPA or Regional data, giving you an immediate roadmap of what's possible.
Does get_parameter_values help me avoid API errors? +
Yes, it does. Instead of guessing if 'Quarterly' is coded as Q or something else, this tool provides the exact valid code for any parameter you need to use in your query.
What should I call when I want actual GDP data? +
You must first use get_dataset_list to narrow down the correct dataset, then follow up with get_parameter_list and finally pass all validated inputs to get_data.
Is BEA MCP only for NIPA data? +
No. While NIPA is a core offering, the MCP can access multiple dataset types, including Regional and International Transaction data, so it covers most US economic areas.
How do I get started using my API key with any BEA function? +
You must first subscribe and provide your unique BEA API Key. Your AI client needs this credential to authenticate every request, whether you're calling get_dataset_list or running a complex query.
What if I don't know the required filters for get_data? +
Run get_parameter_list first. This tool shows all valid parameters and variables for a dataset, letting you build your full data query safely before hitting the main get_data function.
When I use get_data, what format does the economic data come in? +
The output is raw, structured statistical data. Your agent receives clean key-value pairs and tables that you then analyze or pass to another system for interpretation.
Are there limits on how often I can use get_data? +
Yes, the API enforces usage quotas; hitting these limits will cause errors. You need to manage your calls and wait for rate limit resets if you process a lot of data.
How can I see which economic datasets are available? +
You can use the get_dataset_list tool. It will return a comprehensive list of all datasets currently supported by the BEA API, such as NIPA, NIUnderlyingDetail, and FixedAssets.
How do I know what filters or parameters to use for a specific dataset? +
First, use get_parameter_list with the dataset name to see required fields. Then, use get_parameter_values to find valid inputs (like specific Year or TableID) for those parameters.
Can I fetch actual GDP or income figures directly? +
Yes. Use the get_data tool. You will need to provide the dataset name and a JSON string of parameters (e.g., TableName, Frequency, Year) to retrieve the specific economic observations.
Use it with your favorite AI tools
Connect this server to Cursor, Claude, VS Code, and more.