US Macro

US-Macro-MCP

An MCP server that gives AI assistants clean access to US macroeconomic data — 58 indicators covering inflation, employment, growth, housing, consumer behaviour, interest rates, financial stress, markets and federal finances, sourced from the Federal Reserve's FRED database.

It is built so the assistant never has to know a FRED series ID, and never has to do arithmetic. Ask for cpi and you get the latest reading, the previous one, the year-ago value, and the percentage changes already calculated.

Registry Official Link : https://pypi.org/project/us-macro-mcp/

Changelog

Date	Version	Comment
2026-07-27	0.1.2	Added OS and Python version classifiers, changelog, and contact address switched to GitHub noreply.
2026-07-27	0.1.1	Added mcp-name marker for registry ownership verification. First version listed in the official MCP registry.
2026-07-27	0.1.0	Initial release — 58 indicators, 8 tools. Yanked; superseded by 0.1.1.

What it does

Tool	What you use it for
list_indicators	See every indicator available, grouped by category
get_indicator	The current reading of one indicator, with its changes
get_history	A full time series, for trends and charting
compare_indicators	Two or more indicators over the same window, normalised for comparison
release_calendar	Which economic reports are scheduled over the coming days
macro_snapshot	The whole economy — or one category — in a single call
recession_signals	The Sahm rule, yield curve, credit spreads, claims and financial conditions, each graded
search_indicators	Find an indicator by keyword when you don't know its name

Requirements

A free FRED API key. Request one at fredaccount.stlouisfed.org/apikeys — it takes about a minute and costs nothing.

Install

Codex plugin marketplace

Install uv and make sure uvx is on the PATH available to Codex. The plugin launches the published PyPI release us-macro-mcp==0.1.2 locally over stdio; it requires Python 3.10 or newer. Its launcher constrains the MCP SDK to >=1.2,<2 because this release uses the SDK's v1 FastMCP API.

1. Make your FRED_API_KEY environment variable available to the Codex app before starting it. The plugin forwards that variable to the MCP server; it does not store the key in its files or prompt for it during installation. On macOS, run this locally, then fully quit and reopen Codex:

   launchctl setenv FRED_API_KEY 'YOUR_FRED_API_KEY'
   

   Replace the placeholder with your own key. Do not commit or share the key. launchctl setenv applies to the current login session; repeat it after logging back in. On Linux, export the variable before launching Codex from that shell; on Windows, set a user environment variable and restart Codex.

2. In Codex, open Plugins → Add → Add plugin marketplace.

3. Set Source to Signal-Drifts/US-Macro-MCP (or https://github.com/Signal-Drifts/US-Macro-MCP.git), set Git ref to main, leave Sparse paths empty, and click Add marketplace.

4. Select Signal Drifts and install US Macro, then start a new chat.

5. Try: “Use US Macro to list the available indicators,” then ask for a macro snapshot. Listing indicators checks discovery; live data also requires the FRED key and network access to FRED.

You can also register the marketplace with the Codex CLI:

codex plugin marketplace add Signal-Drifts/US-Macro-MCP --ref main

The catalog is at .agents/plugins/marketplace.json; the plugin manifest and MCP configuration are under plugins/us-macro-mcp/. The plugin package has its own version: plugin 0.1.4 uses PyPI server 0.1.2. When updating the server release, update the pinned PyPI version in the plugin's .mcp.json and bump the plugin manifest version, then refresh the marketplace and installed plugin.

This repository provides a custom marketplace. Inclusion in OpenAI's public plugin directory is a separate submission and review process; local MCP submissions currently require an approved partner organization. See the official plugin packaging guide and desktop-only submission guide.

The existing MCP Registry identifier io.github.hgus107/us-macro-mcp is retained; moving the GitHub repository does not migrate that registry identity. Repository links in future Python/MCP Registry releases point to Signal-Drifts; metadata already published on PyPI remains attached to its existing release.

Claude Desktop

Add this to your claude_desktop_config.json:

{
  "mcpServers": {
    "us-macro": {
      "command": "uvx",
      "args": ["--from", "us-macro-mcp==0.1.2", "--with", "mcp[cli]>=1.2,<2", "us-macro-mcp"],
      "env": { "FRED_API_KEY": "your_key_here" }
    }
  }
}

Claude Code

claude mcp add us-macro --env FRED_API_KEY=your_key_here -- uvx --from us-macro-mcp==0.1.2 --with 'mcp[cli]>=1.2,<2' us-macro-mcp

Any other MCP client

With FRED_API_KEY set in the environment, run:

uvx --from us-macro-mcp==0.1.2 --with 'mcp[cli]>=1.2,<2' us-macro-mcp

The server speaks MCP over stdio.

How it behaves

It runs locally. Nothing is hosted. Your client launches the server as a subprocess on your own machine, using your own API key. No data leaves your computer except the read-only requests to FRED.

It caches. A single macro_snapshot fetches 19 indicators at once, and conversations tend to ask for the same series repeatedly. Answers are held in memory for a period matched to how often the number can actually change — an hour for daily series like Treasury yields, half a day for monthly series like CPI. The cache is discarded when the process exits. Adjust CACHE_SECONDS in fred.py if you want different behaviour.

It never logs your API key. Requests are logged as endpoint, series and status code only, and any error text is stripped of the key before it is written or returned.

Known limitations

• The ISM Manufacturing and Services PMI are not available. ISM licenses its data commercially and withdrew it from FRED. The same applies to the Conference Board Consumer Confidence Index. There is no free source for either.
• Rates are national averages, not per-bank. The 30-year mortgage rate is Freddie Mac's weekly survey across thousands of lenders — not any particular bank's advertised rate. Per-institution rates are not published free by anyone.
• FOMC meeting dates are not included. FRED publishes data release schedules, not the Federal Reserve's meeting calendar.
• Michigan consumer sentiment lags. As of this writing FRED's UMCSENT and MICH series are running about two months behind other monthly indicators. The latest_date field always tells you what you actually got.
• Data is reported for the period it covers. June CPI is dated 2026-06-01 and publishes in mid-July. Always read latest_date rather than assuming the most recent month exists yet.

Data source and disclaimer

All data comes from FRED, maintained by the Federal Reserve Bank of St. Louis, which aggregates it from the Bureau of Labor Statistics, the Bureau of Economic Analysis, the Census Bureau, the Federal Reserve Board and others.

The recession_signals tool applies well-known statistical thresholds to public data. It is not a forecast, and nothing this server returns is investment advice.

Annual maintenance checklist

The Federal Reserve publishes its meeting schedule about two years ahead, and Python, the MCP SDK and FRED's own catalog all drift over a year. Work through this list every January and cut a new release.

Data

• Refresh the FOMC schedule in src/us_macro_mcp/fomc.py from federalreserve.gov/monetarypolicy/fomccalendars.htm, and move VALID_THROUGH forward. The next_fomc_meeting field goes empty once the list runs out.

• Re-validate every series ID in the catalog. FRED retires series without warning — the ADP employment series was discontinued this way, returning a valid response with zero observations:

  cd ~/Downloads/Apps/US-Macro-MCP && uv run python -c "
  import asyncio, logging
  logging.getLogger('us_macro_mcp').setLevel(logging.WARNING)
  from us_macro_mcp import catalog, fred
  async def main():
      sem = asyncio.Semaphore(8)
      async def check(i):
          async with sem:
              try:
                  p = await fred.get_observations(i.series_id, start='2025-01-01', frequency_hint=i.frequency)
                  return (i.name, 'OK' if p else 'EMPTY', p[-1]['date'] if p else '-')
              except Exception as e:
                  return (i.name, 'FAIL: ' + str(e)[:70], '-')
      rows = await asyncio.gather(*(check(i) for i in catalog.CATALOG))
      print('problems:', [r for r in rows if r[1] != 'OK'])
  asyncio.run(main())
  "
  

• Check whether anything previously unavailable has become free — particularly the ISM PMI and the Conference Board Consumer Confidence Index, both currently absent from FRED.

• Check FRED's API terms and rate limit (currently 120 requests per minute per key) for changes.

Dependencies

• Bump the mcp SDK and read its changelog for breaking changes. FastMCP is part of the official SDK, so a major version there can change how tools are declared or how the server starts.
• Check whether the MCP specification has revised its transports or tool schema.
• Bump httpx.
• Review requires-python. Drop versions that have reached end of life, and confirm the current floor still installs cleanly.

Release

• Run the full test sweep: all eight tools, plus the negative and boundary cases.
• Bump the version in pyproject.toml and in server.json. They must match or the registry rejects the publish.
• uv build and uv publish to PyPI.
• mcp-publisher publish to the official MCP registry.
• Tag the release on GitHub.

License

MIT

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