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FXMacroData

by io.github.fxmacrodata

Indicator Intelligence Task

indicator_intel_task
Read-only

Build an intelligence pack for one indicator by combining chart-ready series data, derived analytics, and nearest release timing context. Supports MCP Tasks for async execution when clients send task-augmented requests.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
currencyYes3-letter ISO currency code (case-insensitive). 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.
indicatorYesIndicator slug for the given currency. 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.

TDQS

A3.9/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description does not contradict them. It adds useful behavioral context by noting the tool combines data and analytics and supports MCP Tasks async execution when clients send task-augmented requests. It does not cover auth, rate limits, or return envelope, but the presence of annotations lowers the bar.

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

Conciseness5/5

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

Two sentences; the first front-loads the core action and component list, the second adds the async execution behavior with minimal wording. No filler or repetition of schema content.

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

Completeness3/5

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

The description conveys the high-level deliverable (an intelligence pack) and its three constituent data types, and param/coverage and annotations are handled by structured fields. However, with no output schema, the description does not specify the structure or contents of the returned pack beyond those three components, nor how start_date/end_date shape the result. That gap makes it only minimally complete for an agent deciding whether to invoke it.

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

Parameters3/5

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

Schema description coverage is 100%, with each parameter (currency, indicator, dates) fully documented in the input schema. The tool description itself does not mention parameters or add syntax/format details beyond what the schema provides, so baseline 3 applies.

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 description uses a specific verb phrase 'Build an intelligence pack' and specifies the resource ('for one indicator') and scope, distinguishing it from multi-indicator or pair tools like macro_research_pack_task and pair_intel_task. It enumerates the three components—chart-ready series data, derived analytics, release timing context—making the tool's function concrete. The title also reinforces the intent.

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

Usage Guidelines3/5

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

The description implies this tool is for creating a synthesized pack for a single indicator, but does not state when to choose it over sibling tools like indicator_query or indicator_visual_artifact. There are no explicit use-case conditions, prerequisites, or alternatives mentioned, only the async execution note. This is adequate but leaves selection to inference.

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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