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FXMacroData

by io.github.fxmacrodata

FX Spot Rates

forex
Read-only

Get raw historical FX spot-rate rows for a currency pair (e.g. EUR/USD, USD/JPY). Prefer this tool when the user explicitly wants a plain-text table, raw rows, exact values, JSON-like data, or technical-indicator series (SMA, EMA, RSI, MACD, Bollinger Bands, etc.) computed from spot without a chart. If the user asks more generally to show/tell/explain the last few weeks or months of a pair, prefer forex_visual_artifact instead so the client can render a chart. Daily granularity from official central-bank reference rates with full multi-year history. Supported currencies (use lowercase 3-letter codes): AUD, BRL, CAD, CHF, CNH, CNY, DKK, EUR, GBP, ILS, JPY, NGN, NOK, NZD, PEN, SEK, THB, USD. Optional indicators parameter accepts a comma-separated list of technical indicator slugs to attach to each row. Supported indicator values: adx_14, atr_14, bollinger_bands, cci_20, donchian_20, ema_12, ema_20, ema_200, ema_26, ema_50, macd, macd_histogram, macd_signal, rsi_14, sma_20, sma_200, sma_50, stochastic_14_3, williams_r_14, all.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
baseYesBase currency, 3-letter ISO code (case-insensitive). Supported: AUD, BRL, CAD, CHF, CNH, CNY, DKK, EUR, GBP, ILS, JPY, NGN, NOK, NZD, PEN, SEK, THB, USD.
quoteYesQuote currency, 3-letter ISO 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. Defaults to today.
indicatorsNoComma-separated technical-indicator slugs to attach to each row. Supported: adx_14, atr_14, bollinger_bands, cci_20, donchian_20, ema_12, ema_20, ema_200, ema_26, ema_50, macd, macd_histogram, macd_signal, rsi_14, sma_20, sma_200, sma_50, stochastic_14_3, williams_r_14, all.
start_dateNoInclusive lower bound, YYYY-MM-DD. Defaults to ~5 years ago.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true, openWorldHint=true, destructiveHint=false; the description adds source (official central-bank reference rates), granularity (daily), history depth (multi-year), and indicator attachment. No contradiction with annotations; could mention rate limits but not required.

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?

Front-loaded with purpose and usage guidance, but the full supported currency and indicator lists are duplicated verbatim from the schema, adding bulk without new information. Still organized and free of fluff; not overly padded.

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

Completeness4/5

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

With an output schema present and annotations covering the safety profile, the description adequately covers data source, granularity, history, alternatives, and options. No major gaps.

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 covers all five parameters with descriptions at 100%, so baseline is 3. The description largely repeats the currency and indicator lists from the schema, though it adds the 'attach to each row' nuance and lowercase-code hint; no significant additional parameter semantics.

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?

Description uses a specific verb-resource pair ('Get raw historical FX spot-rate rows') and clarifies the output style ('plain-text table, raw rows, exact values'). It differentiates from the sibling forex_visual_artifact by explicitly calling out chart-free use cases.

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?

States 'Prefer this tool when...' with explicit raw-data triggers and tells the agent to use forex_visual_artifact for general chart-friendly asks. Also gives supported indicator behavior, making selection criteria unambiguous.

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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