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FXMacroData

by io.github.fxmacrodata

Quant Scenario Lab Task

quant_scenario_lab_task
Read-only

Run an expanded quant-style policy scenario for an FX pair with deterministic projection, stress percentiles, and horizon assumptions. Supports MCP Tasks for async execution when clients send task-augmented requests.

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_dateNoOptional inclusive upper bound, YYYY-MM-DD.
shock_bpsNoPolicy shock size in basis points (100 bps = 1.00 percentage point).
shock_legNoWhich leg receives the policy shock: base or quote.base
start_dateNoOptional inclusive lower bound, YYYY-MM-DD.
horizon_daysNoScenario horizon in calendar days.
elasticity_per_100bpsNoHeuristic percent FX move per 100 bps spread change.
annualized_volatility_pctNoAnnualized volatility assumption (percent) for stress-band construction.

TDQS

A3.7/5.0
Behavior4/5

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

The description discloses key computational behaviors: deterministic projection, stress percentiles, horizon assumptions, and async execution support. Since annotations already declare readOnlyHint=true and destructiveHint=false, the added context about computation methods and async capability provides value beyond the structured metadata.

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?

The description is two sentences totaling ~35 words, front-loaded with the core purpose and augmented by the async note. No redundant or filler 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?

With 9 parameters and no output schema, the description gives a high-level view of what the tool does but doesn't detail output structure or edge cases. It lists key outputs (projection, stress percentiles, horizon assumptions) but remains somewhat vague for a complex tool lacking an output schema.

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?

All 9 parameters are fully described in the schema, so the baseline is 3. The description adds minimal extra information about parameters, though 'horizon assumptions' hints at horizon_days. Overall, the schema does the heavy lifting.

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

Purpose4/5

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

The description clearly states the tool 'Run[s] an expanded quant-style policy scenario for an FX pair' with specific outputs (deterministic projection, stress percentiles, horizon assumptions). It uses a specific verb and resource, but doesn't explicitly differentiate from the similar sibling 'policy_scenario_modeler_task'.

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?

It mentions support for MCP Tasks for async execution when clients send task-augmented requests, providing a concrete usage context. However, it doesn't provide guidance on when to choose this tool over alternatives like 'policy_scenario_modeler_task' or 'fx_backtest_task'.

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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