Goal: connect FXMacroData tools to a ChatGPT conversation or use the ready-made Custom GPT, then return a source-linked macro brief with explicit series and reference periods.
1. Connect FXMacroData as a native MCP tool source
For a tool-connected conversation, add the hosted FXMacroData MCP server through the custom MCP connection flow available to your account. OpenAI's connection guide describes the current Plugins flow and legacy Apps setup. Availability and creation permissions depend on the account and workspace.
Use https://mcp.fxmacrodata.com as the remote server URL. Configure the offered authentication method, review the discovered tools, and select the resulting connection in a new conversation. Begin with the free USD path; use supported OAuth or subscription credentials for protected data. Keep credentials in connection settings rather than in the prompt.
- Add the remote MCP connection.
- Select its tools in the conversation.
- Retrieve observations and retain their source context.
- Write a brief that distinguishes observations from interpretation.
Use FXMacroData to retrieve the latest US inflation observation.
Report the reference period, value, unit and returned source link.
List confirmed USD events scheduled this week in UTC.
Identify unavailable fields and any access delay.
Keep the evidence separate from your interpretation.Inspect the calls and results before relying on the prose. A native MCP connection, a Custom GPT and an Actions schema are separate surfaces; configuring one does not install the others.
| Surface | When to use it |
|---|---|
| Native MCP connection | Discover and call FXMacroData tools from a selected ChatGPT connection. |
| Ready-made Custom GPT | Start with the supplied research assistant linked below. |
| Custom GPT Actions | Configure an owned GPT with a defined REST schema and authentication. |
| Direct REST | Retrieve a known series from deterministic application code. |
The AI installation guide provides the Actions schema route. A direct REST request for an application uses header authentication:
curl "https://api.fxmacrodata.com/v1/announcements/usd/inflation?limit=1" \
-H "X-API-Key: YOUR_API_KEY"2. Use the ready-made Custom GPT
Open the ready-made assistant: Forex Macro Research FXMacroData Custom GPT.
Start with the Custom GPT when you need a quick FX macro brief, release-risk checklist, central-bank comparison, or data workflow plan. Use the API or dashboard when the output needs to become a chart, notebook, model input, or production research artifact.
What the FXMacroData Custom GPT is for
The FXMacroData Custom GPT is built for traders, analysts, and developers who want to ask macro questions in natural language without starting from an endpoint list. Instead of opening several tabs to check the calendar, central-bank context, inflation history, and pair dashboard, you can start with a single research prompt and then follow the links or API paths that matter.
It is especially useful for questions like:
- What is the macro setup for EUR/USD this week?
- Which upcoming events could move USD pairs?
- How should I compare the Federal Reserve and Bank of Japan policy backdrop?
- Which FXMacroData endpoint should I use for an inflation, policy-rate, or release-calendar workflow?
The GPT is not a replacement for a repeatable data pipeline. Treat it as the front door for research framing. Once the question is clear, move into the relevant FXMacroData surface: pair dashboard, indicator page, release calendar, REST API, or MCP server.
Prerequisites
- A ChatGPT account that can open Custom GPTs.
- An FXMacroData account if you want access to premium API history, non-USD releases, COT, commodities, and MCP tools.
- A clear research objective: pair view, central-bank comparison, event-risk plan, API integration, or notebook workflow.
Quick start
- Open the FXMacroData Custom GPT.
- Ask one specific market question, not a broad request for "everything happening in FX."
- Ask the GPT to list the FXMacroData dashboard or endpoint that supports each claim.
- Verify the key data point in the relevant dashboard, docs page, or REST API response.
- Move repeatable work into code or MCP once the workflow is stable.
The best prompt format
Use prompts that name the pair, horizon, macro driver, and output format. That keeps the GPT focused on the decision rather than producing a generic market note.
For [currency pair], over [time horizon], summarize [macro driver], list [release risks], and show which FXMacroData pages or endpoints I should verify.
Good examples:
- For USD/JPY, give me a two-day event-risk brief with the next three scheduled releases and what would count as a dollar-positive surprise.
- Compare the Federal Reserve and Bank of Japan policy backdrop, then list the indicators I should verify before trading the pair.
- Build a morning prep checklist for AUD and NZD using the release calendar, inflation history, and policy-rate context.
- Tell me which API endpoint I should use to pull US inflation history into a Python notebook.
Weak prompts are vague. "What is happening in forex?" gives the model too much room to summarize rather than analyze. "Give me the next AUD/USD event-risk checklist with verification links" is much better.
Workflow 1: Morning FX macro prep
Use this workflow when you want a fast read before the trading day starts.
- Ask for the regime: "Summarize the macro regime for major USD pairs this week in five bullets."
- Ask for catalysts: "List the scheduled releases that could change that view."
- Ask for verification paths: "For each catalyst, give me the FXMacroData page or endpoint to check."
- Open the source surface: use the release calendar, dashboard, or indicator docs to confirm the data before acting on it.
This is where the Custom GPT is strongest. It reduces the time between "what should I look at?" and "which data point matters?" without forcing you to start in a spreadsheet or API client.
Workflow 2: Pair-specific event risk
For a pair such as EUR/USD, ask the GPT to separate macro drivers into base-currency and quote-currency risks. That structure helps avoid a common mistake: treating every headline as a dollar story.
Example prompt:
For EUR/USD, split this week's event risk into EUR drivers and USD drivers.
For each driver, explain the likely bullish and bearish surprise path.
Then list the FXMacroData dashboard or endpoint I should use to verify it.The useful output should look like a short decision tree: event, currency affected, upside surprise, downside surprise, and verification page. If the GPT gives you a narrative only, ask it to convert the answer into a table.
Workflow 3: Central-bank comparison
Central-bank questions work well because the GPT can help you organize policy signals, inflation pressure, growth context, and upcoming communication risk.
Example prompt:
Compare the ECB and Bank of England for FX traders.
Focus on policy-rate direction, inflation pressure, upcoming releases,
and what would change the relative EUR/GBP macro view.Then verify the relevant indicator pages. For example, use ECB policy rate and UK inflation when the answer depends on rate path or price pressure. Use the dashboard press-release pages when the risk is more about communication than printed data.
Workflow 4: Turn a chat answer into an API request
The Custom GPT is helpful for choosing the right endpoint, but reproducible research should use the REST API directly. That gives you stable output you can put into a notebook, dashboard, alert, or backtest.
Example request:
curl -H "X-API-Key: YOUR_API_KEY" "https://api.fxmacrodata.com/v1/announcements/usd/inflation"Interpret the returned fields before using them in a model:
| Field | Interpretation |
|---|---|
date | The observation's reference-period date. |
val | The numeric observation, interpreted with the series unit. |
announcement_datetime | The stored announcement timestamp, where supplied. |
| Vintage and availability evidence | The additional context needed to establish which version could be used at a historical decision time. |
A stored announcement timestamp does not by itself establish when every vintage became available. For point-in-time research, retain the relevant version and its availability evidence. If that evidence is missing, do not substitute the reference-period date or an assumed publication time.
Workflow 5: Move into MCP for agent workflows
Use the FXMacroData MCP server when you want a tool-connected agent rather than a one-off ChatGPT conversation. MCP is better for repeated prompts, coding environments, and workflows where the agent needs to call structured tools instead of only explaining what to do.
An example remote configuration for a host that uses a servers object is below. Use the schema required by the installed client:
{
"servers": {
"FXMacroData": {
"type": "http",
"url": "https://mcp.fxmacrodata.com"
}
}
}Use this split:
| Need | Best FXMacroData surface | Why |
|---|---|---|
| Fast research question | Custom GPT | Lowest-friction natural-language start. |
| Chart and market context | Dashboard | Best for visual verification and pair context. |
| Notebook or model input | REST API | Structured, repeatable JSON output. |
| Tool-connected AI workflow | MCP server | Lets compatible agents retrieve macro context directly. |
Prompt library for FX traders
These prompts are designed to produce actionable research plans rather than broad market commentary.
Event-risk prompt
Give me the next 72 hours of event risk for USD/JPY.
Rank events by likely FX impact, explain the surprise direction,
and include FXMacroData verification links.Inflation prompt
Summarize the latest inflation trend for USD, EUR, and GBP.
Focus on what changed in the last three releases and how it affects central-bank pricing.Developer prompt
I am building a Python notebook for macro event analysis.
Which FXMacroData endpoints should I call for inflation, policy rates,
release calendar events, and pair context?Backtest prompt
Design a no-lookahead backtest workflow using FXMacroData.
Show how to align eligible vintages by their availability evidence,
then tell me what data should not be joined by calendar date alone.Common mistakes to avoid
- Asking for too much at once: one pair and one horizon usually beats a global market sweep.
- Skipping verification: use the GPT to frame the question, then verify the key data in FXMacroData.
- Confusing reference periods with availability: a historical decision can use only the vintage supported by the availability evidence at that time.
- Leaving outputs unstructured: ask for tables, checklists, and API paths when you need to act on the answer.
- Using chat for production data pulls: once the workflow is stable, move the data pull into REST API or MCP.
Troubleshooting
The answer is too generic. Add a pair, time horizon, and output format. For example: "For USD/JPY over the next 72 hours, give me a table of event risks, surprise paths, and verification links."
The answer does not cite a data surface. Ask: "Which FXMacroData endpoint or dashboard page supports each claim?"
You need code, not prose. Ask the GPT to convert the workflow into a REST API request, Python snippet, or MCP tool workflow.
You need historical testing. Ask specifically for point-in-time handling and verify the vintage and availability fields required by the experiment.
Summary
Use the FXMacroData Custom GPT to clarify the macro question, identify the relevant releases and indicators, and decide which dashboard or endpoint matters. Then use FXMacroData dashboards, REST API, or MCP to verify and operationalize the result.
Start here: open the FXMacroData Custom GPT in ChatGPT. For structured access, continue with the API documentation, the MCP server guide, or a live dashboard such as EUR/USD.
AI was used for research, creating images and editing.
