How to Use Amazon Bedrock AgentCore with FXMacroData for FX Trading Research

Bedrock Agents and AgentCore have different jobs

Amazon Bedrock gives teams a managed agent surface with action groups that define what an agent may ask an application to do. Amazon Bedrock AgentCore adds an MCP-oriented runtime path for agents and tools. Both can be useful for an FX research workflow, but they should be treated as control surfaces, not as the source of market facts.

The useful boundary is straightforward. FXMacroData retrieves the evidence, including current macro releases, forecasts, and calendar context. Bedrock decides whether to request one of the approved actions, then turns the returned facts into a structured brief for a reviewer. A question about the Federal Reserve, a confirmed US CPI release, or the next item on the release calendar should never rely on model memory alone.

Choose REST, action groups, or MCP deliberately

Use the fewest moving parts that support the workflow. The table below separates the Bedrock-specific control surface from the FXMacroData data connection.

For most production applications, start with a Bedrock action group backed by a controlled REST helper. It gives the team one place to enforce authentication, narrow inputs, cache repeated reads, and record each research run. Add AgentCore MCP only when the agent host genuinely benefits from tool discovery. An MCP connection is not a reason to widen what the model can do.

Prerequisites

• An AWS account and a Bedrock or AgentCore environment approved for the models and regions your team uses.
• An FXMacroData API key for protected historical or multi-currency workflows, stored in a server-side secret manager.
• A small service or action-fulfillment layer that can call FXMacroData and record evidence returned to the agent.
• A reviewer or policy rule that can accept, challenge, or reject the resulting research brief.

How to build the workflow

1. Define read-only macro actions

Begin with a question the agent can answer from evidence, such as: "Which confirmed USD events should a reviewer watch before the next London-New York overlap?" Link the response to FX sessions for operating context, but keep the action read-only. Good first actions fetch a calendar window, a single indicator history, or current pair context. They do not submit orders, amend limits, or calculate a position size.

Keep action inputs small and explicit: currency, indicator, date range, and a bounded row limit. A narrow contract makes it easier to test which evidence the agent saw and to reject an attempt to use an unrelated endpoint.

2. Make FXMacroData REST the evidence adapter

Your action-fulfillment service should fetch macro data, return the source path, and retain the quality metadata alongside the response. The model should receive the returned object, not an API key or a prompt that asks it to invent a URL.

import os
import requests

def get_us_inflation_context() -> dict:
    response = requests.get(
        "https://api.fxmacrodata.com/v1/announcements/usd/inflation",
        params={"api_key": os.environ["FXMD_API_KEY"], "limit": 12},
        timeout=20,
    )
    response.raise_for_status()
    payload = response.json()
    return {
        "source_path": "/v1/announcements/usd/inflation",
        "response": payload,
        "data_quality": payload.get("data_quality", {}),
    }

Use production URLs in action fulfillment and keep the key server-side. The returned response should remain available to the review process so a reader can distinguish released facts from the agent's interpretation.

3. Define an action contract the model cannot widen

Bedrock action groups work best when they expose a limited set of predictable inputs. The following is a useful shape for an application-level action contract. Translate the same intent into the current Bedrock action-group configuration for your environment.

{
  "name": "get_macro_release_context",
  "input": {
    "currency": "usd",
    "indicator": "inflation",
    "limit": 12
  },
  "output": ["source_path", "response", "data_quality"]
}

Do not offer a generic URL parameter, arbitrary query body, or account-changing action. A small tool contract is a risk control as well as an implementation convenience.

4. Pass an evidence packet, then require structured output

After the action returns, package the request and response into an evidence object. Ask the model to separate what is confirmed from what is an interpretation. The result should clearly state when the source does not contain enough information to support a conclusion.

{
  "facts": ["Confirmed releases from the returned FXMacroData payload."],
  "scenario_notes": ["Potential review points around confirmed event windows."],
  "unknowns": ["No future release outcome is present in the source."],
  "source_paths": ["/v1/announcements/usd/inflation"],
  "review_status": "human_review_required"
}

This is materially more useful than a free-form trade recommendation. It produces an object that can be compared across models, audited after an event, and improved when a reviewer identifies an error.

5. Use AgentCore MCP only when the runtime is MCP-native

AgentCore documents an MCP contract for agents and tool servers. Use that path when the runtime is designed to discover and invoke MCP tools. Connect the hosted FXMacroData server at https://mcp.fxmacrodata.com through a compatible client or gateway, and test a real tool call before relying on it for research operations.

Do not repackage the hosted FXMacroData server merely to make it look local to an AWS deployment. If the agent environment cannot consume the remote MCP connection cleanly, keep the controlled REST action. The research contract and review requirements remain the same whichever transport you choose.

6. Measure the workflow as a research control

Run a fixed set of historical and current prompts through the agent before changing the model, action schema, or transport. Measure whether the agent selects the right action, preserves the source path, names missing data, and returns an object a reviewer can use quickly. Do not use style or verbosity as the primary quality signal.

Evidence contract and checks

Common questions

Can Amazon Bedrock AgentCore use FXMacroData?

Yes. A Bedrock action fulfillment layer can fetch FXMacroData REST evidence, while an MCP-native AgentCore workflow can use the hosted FXMacroData MCP server when the runtime supports that connection.

Should a Bedrock research agent use REST or MCP?

Use REST as the production default when the application owns credentials, logging, retries, and validation. Use MCP when the surrounding AgentCore workflow is genuinely MCP-native and benefits from hosted tool discovery.

Can a Bedrock action group place FX orders?

It could be configured to call many kinds of actions, but an FXMacroData research integration should remain read-only. Execution belongs behind separate deterministic controls and explicit approvals.