{"slug": "build-an-economic-data-explorer-with-ai-and-the-fxmacrodata-mcp", "title": "Build an Economic Data Explorer With AI and the FXMacroData MCP", "summary": "A walkthrough published on Stackademic shows developers how to connect Cursor to the FXMacroData MCP server at mcp.fxmacrodata.com and use its data_catalogue and indicator_query tools to retrieve US headline CPI annual inflation for January 1, 2023 through December 31, 2024, then build a local Flask explorer around the saved 24-observation snapshot. The guide instructs the assistant to save the returned data array as data/us_cpi_yoy.json without altering values or field names, alongside the series definition, units, publisher, source links and retrieval metadata, and to report missing coverage rather than filling gaps. The resulting app displays the observation count, the last reading in the selected range and the change from the first to last reading in percentage points, with an explicit empty state when a date range contains no observations.", "body_md": "*Cover image created with AI.*\n\nGive an AI coding assistant a narrow brief: build a page where you can choose a date range and inspect US inflation.\n\nThe page needs three numbers, a table and source links. Change the dates, and the summary should change with them. Choose a period with no observations, and the app should tell you.\n\nThat is a manageable project. It also introduces a useful way to work with AI: let the assistant discover the available data before it builds the interface around it.\n\nThe [FXMacroData MCP](https://fxmacrodata.com/documentation/mcp-server?utm_source=medium&utm_medium=referral&utm_campaign=stackademic&utm_content=economic_data_explorer_mcp_docs) gives the coding assistant tools for that discovery and retrieval. You can ask it to inspect an economic series, save a bounded set of observations and build a small application around the returned fields.\n\nThis walkthrough uses Cursor as the coding environment and Flask for the app. The first version is a local explorer of a saved historical snapshot.\n\nCreate a project folder and add .cursor/mcp.json:\n\n```\n{  \"mcpServers\": {    \"fxmacrodata\": {      \"url\": \"https://mcp.fxmacrodata.com\"    }  }}\n```\n\nEnable the server under **Customize → MCP** and complete authentication. Your FXMacroData access must cover the series and historical window you request. Keep credentials in the client’s supported private authentication configuration.\n\nCursor’s [MCP documentation](https://cursor.com/docs/mcp) explains project configuration, authentication and tool approval. Before approving a data request, inspect its arguments so the scope matches your project.\n\nStart with a discovery prompt:\n\n*Use the FXMacroData MCP data_catalogue tool to identify US headline CPI annual inflation. Confirm the indicator name, units, frequency and historical coverage. Show me the series definition before building the app.*\n\nYou are establishing what the interface will mean. A card labelled “Inflation” is much more useful when the page also says whether it represents headline CPI, its annual percentage change and the observation period.\n\nFor the first version, use January 2023 through December 2024: a monthly series with 24 expected observations.\n\nAsk the assistant:\n\n*Use the FXMacroData MCP indicator_query tool to retrieve the confirmed US headline CPI annual inflation series from January 1, 2023 through December 31, 2024. Set the page limit to 100. Inspect the returned dates and pagination and confirm whether all 24 monthly observations are present. Save the returned data array as data/us_cpi_yoy.json without changing the values or field names. Save the series definition, units, publisher, source links, request parameters and retrieval metadata alongside it. Report missing coverage rather than filling gaps.*\n\nThe relevant MCP request is:\n\n```\nindicator_query(    currency=\"usd\",    indicator=\"inflation\",    start_date=\"2023-01-01\",    end_date=\"2024-12-31\",    limit=100)\n```\n\nThat call belongs to the assistant’s tool workflow. The app you are about to build will read the saved observations.\n\nThis keeps the first version easy to inspect. You can change the layout, fix a calculation or experiment with filters using the same dataset. A browser refresh does not need another retrieval of the history.\n\nGive the assistant a specification it can check:\n\n*Build a local Flask app that reads the saved FXMacroData observations. Use Python for the server and ordinary HTML for the page. Add start and end date inputs. Display the number of observations, the last reading in the selected range and the change from the first selected reading to the last. Label that change in percentage points. Show a table with observation dates, values and source links. Include the series definition and snapshot period above it. Display an explicit empty state if the range contains no observations. Validate the dates and handle a one-observation selection without implying a trend. Keep the page usable on a narrow screen. The app should read local data files and should not contain an API key or make data requests when a visitor changes a filter.*\n\nThe wording of the summary cards deserves attention. For a historical explorer, **“Last reading in selection”** tells the reader exactly what is being displayed. A bare “Latest” label could be mistaken for the latest figure available today.\n\nThe source links should come from the returned observations. Displaying those links gives readers a route from the interface back to the publisher’s evidence.\n\nThe central operation is small. Load the observations, sort them and select the requested range:\n\n``` python\nimport jsonfrom pathlib import Path\nrows = json.loads(    Path(\"data/us_cpi_yoy.json\").read_text())rows = sorted(rows, key=lambda row: row[\"date\"])\nstart = \"2024-09-01\"end = \"2024-12-31\"\nselected = [    row for row in rows    if start <= row[\"date\"] <= end]\nchange_pp = (    selected[-1][\"val\"] - selected[0][\"val\"]    if len(selected) > 1    else None)\n```\n\nThis comparison assumes validated ISO dates in YYYY-MM-DD format. In the Flask route, parse the form inputs as dates and reject an invalid or reversed range before filtering.\n\nThe example snapshot contains a 2.4% reading for September 2024 and a 2.9% reading for December 2024. Selecting those months gives four observations and a change of **+0.5 percentage points**.\n\nThe observation dates identify the periods represented by the figures. They are not a substitute for release timestamps or historical availability information.\n\nAsk the assistant to keep the calculation visible in a small function or route. When a card displays an unexpected number, you should be able to find the expression that produced it.\n\nAn attractive page is only part of the project. The filters need to produce sensible results when a reader moves away from the default view.\n\nGive the assistant this follow-up:\n\n*Use Flask’s test client to check the default range, September–December 2024, an empty range, an invalid date, a reversed range and a single-observation range. Use the saved observations for every check. Report the expected behaviour and any failures.*\n\nFor this explorer, the checks are straightforward:**Full 2023–2024 snapshot:** Show 24 observations\n\nThese checks exercise the behaviour behind the summary cards. You should also open the page in a browser and inspect the date controls, table, source links and narrow-screen layout.\n\nOnce the first view works, you can expand it:\n\n*Use the FXMacroData MCP catalogue to identify a suitable US employment series. Show its definition and units. Propose how to add it to the explorer before retrieving a bounded sample.*\n\nThe additional series may need different labels and calculations. A payroll change, unemployment rate and policy rate have different meanings. Let those definitions guide the interface rather than forcing every indicator into the same “percentage change” card.\n\nKeep each saved dataset associated with its own metadata. When a user switches series, the title, units, source and available range should switch together.\n\nIf you eventually want automatic updates or a shared hosted app, decide how retrieval, credentials and refreshes will work as a separate step. A local snapshot explorer is already useful for reviewing a particular historical window.\n\nThe workflow starts with a question, moves through MCP discovery and retrieval, and ends with a small application whose calculations you can inspect. The assistant helps build the interface; the observations determine what it can honestly show.\n\n*Disclosure: I am the founder of FXMacroData.*\n\n*AI was used for research, creating the cover image and editing.*\n\n[Build an Economic Data Explorer With AI and the FXMacroData MCP](https://blog.stackademic.com/build-an-economic-data-explorer-with-ai-and-the-fxmacrodata-mcp-272171137f44) was originally published in [Stackademic](https://blog.stackademic.com) on Medium, where people are continuing the conversation by highlighting and responding to this story.", "url": "https://wpnews.pro/news/build-an-economic-data-explorer-with-ai-and-the-fxmacrodata-mcp", "canonical_source": "https://blog.stackademic.com/build-an-economic-data-explorer-with-ai-and-the-fxmacrodata-mcp-272171137f44?source=rss----d1baaa8417a4---4", "published_at": "2026-10-21 13:53:55+00:00", "updated_at": "2026-10-21 14:13:21.345319+00:00", "lang": "en", "topics": ["ai-tools", "agent-protocols", "ai-agents", "developer-tools"], "entities": ["FXMacroData", "Cursor", "Flask", "Stackademic", "US headline CPI"], "also_reported_by": [], "alternates": {"html": "https://wpnews.pro/news/build-an-economic-data-explorer-with-ai-and-the-fxmacrodata-mcp", "markdown": "https://wpnews.pro/news/build-an-economic-data-explorer-with-ai-and-the-fxmacrodata-mcp.md", "text": "https://wpnews.pro/news/build-an-economic-data-explorer-with-ai-and-the-fxmacrodata-mcp.txt", "jsonld": "https://wpnews.pro/news/build-an-economic-data-explorer-with-ai-and-the-fxmacrodata-mcp.jsonld"}}