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FXMacroData

by io.github.fxmacrodata

Indicator Time Series

indicator_query
Read-only

Get a paginated historical time series of a single macroeconomic indicator for a currency, sourced directly from the official central bank or statistical agency. Use this for CPI/inflation, GDP, unemployment, policy rates, bond yields, payrolls, retail sales, PCE, PPI, trade balance, current account, money supply, and similar series. Each row returns date (value-as-of), val (numeric), and announcement_datetime (when the value was first published — useful for backtest point-in-time integrity). This plain tool returns raw rows for data workflows. Use indicator_visual_artifact when the host should render an MCP App chart. Use limit, offset, or page to page through broad histories; check pagination.next_offset and pagination.page_includes_latest_available in the result. Responses default to official-source rows only; prohibited private aggregator rows are always removed. Always call data_catalogue(currency) first to get the exact indicator slug. USD indicators are free; non-USD requires API key. Supported currencies: AUD, BRL, CAD, CHF, CNH, CNY, DKK, EUR, GBP, ILS, JPY, NGN, NOK, NZD, PEN, SEK, THB, USD. Supported indicators: average_hourly_earnings, average_hourly_earnings_mom, balance_on_goods, balance_on_services, breakeven_inflation_rate, broad_money, building_approvals, building_permits, business_confidence, capital_account_balance, cb_assets, commodity_price_energy, commodity_price_ex_energy, commodity_price_index, commodity_prices, consumer_confidence, core_inflation, core_inflation_median, core_inflation_mom, core_inflation_trim, core_pce, core_pce_mom, credit_growth, crude_oil_inventories, current_account_balance, dairy_exports, deposit_rates, durable_goods_orders, employment, exports, financial_account_balance, foreign_reserves, full_time_employment, fx_reserves, gdp, gdp_growth_q4_yoy, gold_reserves, gov_bond_10y, gov_bond_1y, gov_bond_20y, gov_bond_2y, gov_bond_30y, gov_bond_3y, gov_bond_40y, gov_bond_4y, gov_bond_5y, gov_bond_7y, government_debt, house_price_index, house_prices, household_credit, housing_starts, imports, inflation, inflation_linked_bond, inflation_mom, initial_jobless_claims, international_assets, international_liabilities, job_openings, m1, m2, m3, nairu, natural_gas_storage, net_foreign_asset_position, non_farm_payrolls, non_farm_payrolls_change, part_time_employment, participation_rate, pce, pce_mom, policy_rate, policy_rate_midpoint, policy_rate_mlf, policy_rate_mro, policy_rate_target_lower, ppi, ppi_mom, primary_income_balance, retail_sales, retail_sales_control_group, retail_sales_ex_autos, retail_sales_ex_autos_and_gas, risk_free_rate, secondary_income_balance, sight_deposits, snb_balance_sheet, tankan_capex, terms_of_trade, trade_balance, trade_weighted_index, trimmed_mean_inflation, unemployment, wage_price_index, wages.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
pageNoOne-based page number. When supplied, the REST endpoint derives offset as `(page - 1) * limit`.
slugNoOptional compound `"<currency>:<indicator>"` slug (e.g. `"usd:cpi"`, `"jpy:policy_rate"`). Many small / open tool-calling models concatenate the two parts anyway. When supplied, overrides `currency` and `indicator`.
limitNoMaximum rows to return from the existing REST pagination path. Defaults to 20; maximum 100.
offsetNoZero-based row offset after most-recent-first ordering.
currencyNo3-letter ISO currency code (case-insensitive). Optional if `slug` is provided as a `"usd:cpi"`-style compound. Supported: AUD, BRL, CAD, CHF, CNH, CNY, DKK, EUR, GBP, ILS, JPY, NGN, NOK, NZD, PEN, SEK, THB, USD.
end_dateNoInclusive upper bound, YYYY-MM-DD.
indicatorNoIndicator slug for the given currency. Optional if `slug` is provided as a `"usd:cpi"`-style compound. Supported: average_hourly_earnings, average_hourly_earnings_mom, balance_on_goods, balance_on_services, breakeven_inflation_rate, broad_money, building_approvals, building_permits, business_confidence, capital_account_balance, cb_assets, commodity_price_energy, commodity_price_ex_energy, commodity_price_index, commodity_prices, consumer_confidence, core_inflation, core_inflation_median, core_inflation_mom, core_inflation_trim, core_pce, core_pce_mom, credit_growth, crude_oil_inventories, current_account_balance, dairy_exports, deposit_rates, durable_goods_orders, employment, exports, financial_account_balance, foreign_reserves, full_time_employment, fx_reserves, gdp, gdp_growth_q4_yoy, gold_reserves, gov_bond_10y, gov_bond_1y, gov_bond_20y, gov_bond_2y, gov_bond_30y, gov_bond_3y, gov_bond_40y, gov_bond_4y, gov_bond_5y, gov_bond_7y, government_debt, house_price_index, house_prices, household_credit, housing_starts, imports, inflation, inflation_linked_bond, inflation_mom, initial_jobless_claims, international_assets, international_liabilities, job_openings, m1, m2, m3, nairu, natural_gas_storage, net_foreign_asset_position, non_farm_payrolls, non_farm_payrolls_change, part_time_employment, participation_rate, pce, pce_mom, policy_rate, policy_rate_midpoint, policy_rate_mlf, policy_rate_mro, policy_rate_target_lower, ppi, ppi_mom, primary_income_balance, retail_sales, retail_sales_control_group, retail_sales_ex_autos, retail_sales_ex_autos_and_gas, risk_free_rate, secondary_income_balance, sight_deposits, snb_balance_sheet, tankan_capex, terms_of_trade, trade_balance, trade_weighted_index, trimmed_mean_inflation, unemployment, wage_price_index, wages.
start_dateNoInclusive lower bound, YYYY-MM-DD.
official_onlyNoWhen true, return only official-source rows and remove explicit fallback observations. Prohibited private aggregator rows are always removed.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.7/5.0
Behavior5/5

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

Annotations already mark it read-only and non-destructive, and the description adds substantial behavioral context: row fields including announcement_datetime for point-in-time integrity, pagination fields to inspect, official-source-only default with prohibited rows always removed, and API-key requirements for non-USD. No contradiction with annotations.

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

Conciseness3/5

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

The opening sentences are front-loaded and every substantive section earns its place, but the description repeats the full supported-indicator and currency enumerations that already exist verbatim in the schema. It is organized and dense, yet noticeably over-long.

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?

Covers source, prerequisites, auth, supported universe, pagination, output row semantics, and the alternative rendering tool. Given the rich input/output schemas and annotations, nothing needed to call the tool correctly 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 coverage is 100%, so the baseline is 3, but the description adds cross-parameter guidance: use limit, offset, or page for broad histories, inspect pagination.next_offset and page_includes_latest_available, and understand the official_only default/invariant. It doesn't add new meaning for dates or currency/indicator beyond the schema, but the extra pagination/source-default semantics justify a 4.

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?

States a specific verb ('Get'), resource ('paginated historical time series of a single macroeconomic indicator for a currency'), and sourcing constraint. It also distinguishes itself from indicator_visual_artifact by explicitly saying this returns raw rows for data workflows, so an agent can pick it correctly.

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

Usage Guidelines5/5

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

Explicitly names the conditions and alternatives: use for common macro series, use indicator_visual_artifact for chart rendering, and always call data_catalogue(currency) first to resolve the exact slug. This is direct 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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TDQS

A3.8/5.0
Disambiguation4/5

Most tools have clearly distinct purposes with detailed descriptions; the visual_artifact variants are explicitly duplicate payloads for chart rendering. However, several task and analysis tools (macro_briefing_task, macro_research_pack_task, indicator_intel_task) have overlapping scopes and could cause misselection despite different outputs.

Naming Consistency4/5

Tool names are consistently snake_case with systematic _task and _visual_artifact suffixes, making the pattern predictable. Minor deviations like 'ping', 'subscribe_for_mcp_access', and a few noun-only names (e.g., 'forex', 'commodities') break a strict verb_noun pattern but remain readable.

Tool Count2/5

At 48 tools, the surface is far beyond the typical well-scoped server and risks overwhelming agents. The broad macro/FX domain justifies some size, but 48 is excessive and could be consolidated (e.g., merging visual artifact pairs or grouping task tools).

Completeness5/5

The tool set covers the full macro/FX workflow: data discovery (data_catalogue), raw queries (indicator_query, forex, commodities), visual artifacts, release calendar, news, COT, sentiment, seasonality, backtesting, scenario modeling, portfolio risk, and reference tools. No obvious dead ends or missing lifecycle operations for a read-heavy data server.

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