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Glama

FXMacroData

by io.github.fxmacrodata

Release Calendar

release_calendar
Read-only

Get upcoming scheduled macroeconomic release timestamps for a currency. Use this when the user asks 'when is the next CPI/GDP/payrolls/policy decision', or to plan a trade around a known release. Returns ISO-8601 announcement_datetime values in UTC plus market-local timestamps. Pass timezone for an additional announcement_datetime_requested_timezone field. Each row has a release string with the indicator name and a currency code. Unbounded calls return future releases only; do not show stale past rows unless the user explicitly asks for historical/past calendar data. Consumer-facing clients should present the returned markdown agenda or render the Release Calendar App resource; do not summarize this tool as only a row count. Pass an optional indicator filter to narrow to a single series. Pass optional start_date and end_date bounds when the user mentions a month, week, day, or explicit date range. Supported currencies: AUD, BRL, CAD, CHF, CNH, CNY, COMM, 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
currencyYes3-letter ISO currency code (case-insensitive). Supported: AUD, BRL, CAD, CHF, CNH, CNY, COMM, DKK, EUR, GBP, ILS, JPY, NGN, NOK, NZD, PEN, SEK, THB, USD.
end_dateNoOptional inclusive upper bound, YYYY-MM-DD.
timezoneNoOptional IANA timezone for an additional converted timestamp, for example America/Sao_Paulo.
indicatorNoOptional indicator slug to narrow results. 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_dateNoOptional inclusive lower bound, YYYY-MM-DD.

TDQS

A4.6/5.0
Behavior5/5

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

With readOnlyHint and destructiveHint annotations already covering safety, the description adds meaningful behavioral context: return shape (ISO-8601 announcement_datetime in UTC plus market-local timestamps, release string, currency), the impact of the timezone parameter, and default future-only filtering. It also instructs client-facing presenting behavior, which is useful beyond what annotations provide.

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 core explanatory sentences are tight and front-loaded, but the description duplicates the entire supported currency and indicator enumerations already present in the input schema. This large redundant block adds length without unique value, preventing a higher score.

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?

There is no output schema, yet the description covers return fields, default filtering behavior, all optional parameter semantics, supported values, and client presentation guidance. For a read-only calendar tool, the definition supplies everything an agent needs to call it correctly and interpret results.

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

Parameters5/5

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

Schema coverage is 100%, but the description adds actionable guidance for each optional parameter: timezone produces an extra converted timestamp field, indicator narrows to a single series, and start_date/end_date should be passed when the user mentions a month, week, day, or range. This lifts the agent beyond raw schema descriptions.

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 opens with a precise verb and object: 'Get upcoming scheduled macroeconomic release timestamps for a currency.' It also gives a concrete example query ('when is the next CPI/GDP/payrolls/policy decision') and clarifies it is calendar-focused, clearly distinguishing it from sibling tools like latest_announcements or announcement_changes.

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?

The description explicitly instructs when to use the tool ('Use this when the user asks...') and when not to show stale rows unless explicitly requested. However, it does not name alternative tools or explicitly say when to prefer a sibling, so it falls just short of a 5.

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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