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get_economic_indicators

Read-only

Returns observations of key US macro, energy, and fiscal indicators from four sources: - BLS (Bureau of Labor Statistics): the canonical labor + price statistics. ~20-series watchlist covering unemployment, payrolls, wages, CPI, PPI, productivity. Most monthly, ECI/productivity quarterly. - FRED (Federal Reserve Economic Data, St Louis Fed): rates, money supply, GDP, PCE inflation, mortgage rates, jobless claims, Fed balance sheet, breakeven inflation, dollar index, consumer sentiment. ~30-series watchlist. Some daily (rates, dollar), weekly (mortgage, Fed assets, jobless claims), monthly, quarterly. - EIA (Energy Information Administration): WTI + Brent crude oil spot prices, Henry Hub natural gas, US gasoline retail price, US crude oil production. Unique energy data not in BLS or FRED. Mostly weekly cadence. - FiscalData (Treasury Bureau of the Fiscal Service): total public debt outstanding TO THE PENNY, daily, 1993→present (split into debt held by the public vs intragovernmental); Monthly Treasury Statement gross receipts / outlays / deficit-or-surplus; average interest rate actually paid on each Treasury security class (Bills/Notes/Bonds/TIPS/FRN + nonmarketable, 2001→present). Filter to one source via source: 'bls' | 'fred' | 'eia' | 'fiscaldata'. Default returns all four unified — series_id disambiguates across catalogs. Use this when the user asks about: unemployment rate, jobs report, nonfarm payrolls, CPI / PCE / inflation, Fed Funds rate, Treasury yields, mortgage rates, yield-curve inversion, money supply / M2, Fed balance sheet / QE / QT activity, GDP, housing starts, retail sales, consumer sentiment, jobless claims, trade balance, dollar strength, national debt / debt ceiling levels, monthly federal deficit, interest cost on the debt, or general macro context for cross-source analysis. Categories (for filtering): - rates — Fed Funds, Treasury yields, mortgage, corporate bonds - gdp — Real + nominal GDP, GDP growth rate - activity — Industrial production, housing starts, retail sales - inflation — CPI/PPI (BLS) + PCE/Core PCE/breakevens (FRED) - employment — Unemployment rates (U-3, U-6), payrolls, jobless claims - labor-force — Labor force participation rate - wages — Average hourly earnings, employment cost index - hours — Average weekly hours - productivity — Nonfarm productivity, unit labor costs - money — M2, Fed total assets, overnight reverse repo - debt — Federal debt (FRED quarterly + FiscalData daily to-the-penny), Treasury general account - fiscal — MTS monthly receipts, outlays, deficit/surplus (positive = deficit, negative = surplus, per Treasury's sign convention) - trade — Trade balance, trade-weighted dollar index - sentiment — U Michigan Consumer Sentiment - energy — WTI/Brent crude, Henry Hub natural gas, retail gasoline, US crude production (EIA) Period format is fixed-width per cadence so lexicographic sort = chronological: - 2026M04 (April 2026), 2026Q01 (Q1 2026), 2026A01 (annual 2026), 2026W18 (week 18 of 2026), 2026D258 (day-of-year 258). Set latest_only=true to get one record per series (the most-recent observation) — useful for 'where are things now' snapshot questions. Daily series under latest_only return only the latest day per series (deduped client-side); without it you can pull arbitrary history. Pure-publisher posture: the unit on each series is documented in the unit field; we do not compute year-over-year deltas, seasonally adjust differently, or derive 'real' vs 'nominal' versions — agents do those calculations on top.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNoDefault 50, max 500.
sourceNoFilter to one source. Omit to query all four. BLS = canonical labor + price stats. FRED = rates, money, GDP, PCE inflation, sentiment. EIA = energy prices + production. FiscalData = Treasury debt to the penny, MTS budget totals, avg interest rates on the debt.
sort_byNoDefault period (chronological).
categoryNoBucket filter.
series_idNoExact series ID. BLS examples: 'LNS14000000' (U-3), 'CES0000000001' (payrolls), 'CUUR0000SA0' (CPI). FRED examples: 'DFF' (Fed Funds), 'DGS10' (10Y Treasury), 'PCEPILFE' (Core PCE), 'M2SL' (M2), 'WALCL' (Fed assets), 'UMCSENT' (sentiment). FiscalData examples: 'FISCAL-DEBT-TOTAL-DAILY' (daily national debt), 'FISCAL-MTS-DEFICIT-MONTHLY' (monthly deficit/surplus).
since_yearNoCalendar-year lower bound (inclusive).
sort_orderNoDefault desc.
until_yearNoCalendar-year upper bound (inclusive).
latest_onlyNoWhen true, return only the most-recent observation per series. Useful for 'current state' snapshots.
period_typeNoCadence filter.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.6/5.0
Behavior5/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Annotations cover only the safety profile (readOnlyHint, openWorldHint, destructiveHint), and the description adds substantial behavior beyond that: the 'pure-publisher posture' disclaimer that no YoY deltas or alternate seasonal adjustments are computed, the fact that units live in a `unit` field, the client-side dedup of daily series under latest_only, and the fiscal sign convention (positive = deficit). This is exactly the extra context annotations cannot carry.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the purpose and then organized into scannable bullets (sources, trigger phrases, categories, period format, latest_only, posture), so an agent can find what it needs quickly. It is still long, and the per-source bullets partially restate the `source` enum description in the schema, which is mild redundancy rather than waste.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With no output schema, the description carries the full burden and does so: it explains what a record contains (series, unit field, period), the period encoding, sign conventions, and that raw values are unmodified so agents compute derived metrics themselves. For a 10-parameter, zero-required cross-source tool, nothing essential to correct invocation is missing.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so the baseline is 3; the description goes further by explaining the fixed-width period format (2026M04, 2026Q01, 2026W18, 2026D258) so lexicographic sort equals chronological, describing what each `source` value contains, mapping the `category` buckets to concrete series, and clarifying the `latest_only` snapshot behavior. That materially enriches semantics the schema alone only gestures at.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

The opening sentence states a specific verb+resource (returns observations of key US macro, energy, and fiscal indicators) and immediately names the four publisher sources. It is easily separable from siblings like get_daily_prices, get_fundamentals, or get_treasury_auctions because it scopes itself to macro indicator series across BLS/FRED/EIA/FiscalData.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Provides an extensive explicit trigger list ('Use this when the user asks about: unemployment rate, jobs report, CPI / PCE, Fed Funds rate, national debt...') plus a category taxonomy for filtering, which gives an agent strong positive routing signals. It never states when NOT to use it or names a sibling alternative for overlapping cases (e.g., Treasury auction or price data), so it falls short of full when/when-not guidance.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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