Macro Tools
These tools answer questions like:
- "How is the US economy doing β soft landing or stagflation?"
- "What's the current US inflation reading?"
- "Compare household debt across Thailand, South Korea, and the US"
- "What's the consumer spending backdrop for a Thai retailer?"
- "Is the US labor market still tight?"
Tools at a glanceβ
| Tool | What it does | Credits |
|---|---|---|
macro_pulse | US macro dashboard β fused multi-indicator view by theme | 5 |
find_series | Resolve a macro concept to a FRED series id | Free |
fred_series | Fetch + analyze one FRED time series | 1 |
find_indicator | Resolve a concept to a World Bank indicator id | Free |
wb_series | Fetch + compare World Bank data across countries | 1 |
Two data sources, two jobsβ
FRED (Federal Reserve Economic Data) β US high-frequency macro: inflation, unemployment, GDP, consumer spending, housing, the yield curve. Monthly or quarterly updates. Use for US macro questions and rate-sensitive analysis.
World Bank β Cross-country structural data: GDP per capita, household consumption, household debt, demographics, poverty, development indicators. Annual updates. Use for EM country reads and the cross-border consumer/credit backdrop on a domestic-demand stock.
:::caution Not for rates or the yield curve
"US interest rates" / "will rates rise?" / "is the curve inverted?" β use bond_pulse instead. FRED has rate data, but bond_pulse gives the full curve + positioning + macro news in one call β which is the substance of a rate question.
:::
macro_pulse β US macro dashboardβ
A fused multi-indicator read that answers "how is the US macro economy doing?" for a specific theme. Pulls the relevant FRED series together, computes cross-indicator context, and returns a synthesized regime picture.
Available scope values:
| Scope | What it covers |
|---|---|
economy | Overall regime β growth vs contraction, leading indicators |
inflation | CPI, PCE, core inflation, inflation expectations |
labor | Unemployment, payrolls, job openings, wage growth |
consumer | Retail sales, consumer confidence, personal spending |
housing | Home prices, starts, permits, mortgage rates |
recession-risk | Yield curve, LEI, credit spreads, unemployment trend |
Example: "Is the US in a soft landing or heading for recession?" β macro_pulse(scope="recession-risk").
:::info Cost
macro_pulse costs 5 credits β it bundles multiple FRED series into one synthesized view.
:::
find_series β Resolve a macro concept to a FRED idβ
Translates a natural-language macro concept into the FRED series id needed by fred_series. Accepts any language.
Example inputs:
| Input | FRED id |
|---|---|
"US inflation" | CPIAUCSL |
"core PCE" | PCEPILFE |
"unemployment rate" | UNRATE |
"10-year treasury yield" | DGS10 |
"M2 money supply" | M2SL |
"ISM manufacturing" | MANEMP or NAPM |
Returns ranked candidates β the AI picks the best match and passes the series id to fred_series.
This resolver is free (0 credits).
fred_series β Fetch + analyze a FRED time seriesβ
Fetches a single FRED series and returns the data with a contextual read: current level, percentile vs own history, trend direction, and a plain-language interpretation.
fred_series(series_id="CPIAUCSL") # US CPI β latest + trend
fred_series(series_id="UNRATE", n=24) # unemployment, last 24 months
Key params: series_id (from find_series), n (number of observations), from_/to (ISO date range).
:::caution One series per call
fred_series takes one series_id at a time. To compare two series, call twice and diff the results. Comma-separated ids are rejected.
:::
find_indicator β Resolve a concept to a World Bank indicatorβ
Translates a natural-language structural concept into the World Bank indicator id needed by wb_series. Accepts any language.
Example inputs:
| Input | World Bank indicator |
|---|---|
"household consumption" | NE.CON.PRVT.ZS |
"household debt" | FS.AST.PRVT.GD.ZS |
"GDP per capita growth" | NY.GDP.PCAP.KD.ZG |
"consumer price inflation" | FP.CPI.TOTL.ZG |
"ΰΈ«ΰΈΰΈ΅ΰΉΰΈΰΈ£ΰΈ±ΰΈ§ΰΉΰΈ£ΰΈ·ΰΈΰΈ" (Thai) | FS.AST.PRVT.GD.ZS |
Returns ranked candidates β the AI picks the best match and passes the id to wb_series.
This resolver is free (0 credits).
wb_series β Cross-country structural dataβ
Fetches a World Bank indicator for one or more countries, returning the historical series plus a contextual read β including how each country compares to the others.
wb_series(indicator="FS.AST.PRVT.GD.ZS", countries=["TH","KR","US"])
Key params:
indicatorβ World Bank indicator id fromfind_indicatorcountriesβ ISO-2 list; omit for the global cross-country viewfrom_year/to_yearβ year range (World Bank data is annual)
When to use thisβ
The primary use case is the structural backdrop for a domestic-demand stock. Before concluding on a Thai retailer, a Korean bank, or an Indonesian consumer name, pull the country's household consumption and household debt trend to see if the consumer has capacity to spend.
The key question: does the macro/structural backdrop confirm or contradict the price + news picture?
Combining with investment readsβ
Macro tools alone don't make an investment conclusion β they're the backdrop that sharpens one:
| Stock type | Which backdrop to pull |
|---|---|
| Domestic consumer / retail / bank | find_indicator β wb_series for household consumption + debt |
| Exporter with US exposure | find_series β fred_series for US consumer spending or PMI |
| Rate-sensitive (real estate, utilities) | bond_pulse (preferred) or FRED for 10Y yield |
| Supply-chain exposed | Trade tools (find_hs β search_trade) |
Pull only the 1β2 backdrops that would materially change the conclusion β not all of them by reflex.
Routing guideβ
| You want to⦠| Use |
|---|---|
| "How is the US economy doing?" | macro_pulse |
| "What's US inflation right now?" | find_series β fred_series |
| "Compare household debt across countries" | find_indicator β wb_series |
| "Consumer backdrop for a Thai retailer" | find_indicator β wb_series(countries=["TH"]) |
| "US interest rates / yield curve" | bond_pulse (not macro tools) |