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FXMacroData

by io.github.fxmacrodata

Policy Scenario Modeler Task

policy_scenario_modeler_task
Read-only

Run a policy-rate spread what-if scenario for an FX pair and estimate directional spot impact using an explicit heuristic elasticity assumption. 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.
policy_shock_bpsNoOptional alias for shock_bps for compatibility with host-side app payloads.
elasticity_per_100bpsNoHeuristic percent change in FX spot for a 100 bps spread change. Used as a scenario assumption, not a forecast guarantee.

TDQS

A4/5.0
Behavior4/5

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

Annotations already provide readOnlyHint=true and destructiveHint=false. The description adds behavioral context by clarifying that the tool uses a heuristic elasticity assumption ('not a forecast guarantee') and supports async execution, which goes beyond the annotations without contradicting them.

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 concise sentences: the first states the core functionality, the second notes the async capability. No redundant phrasing or filler; information is front-loaded and easily parsed.

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?

Given the complexity (8 parameters, no output schema), the description covers the tool's purpose and key behavioral aspect (heuristic elasticity). It does not describe the return format, but the extensive schema documentation and annotations compensate. For a task-based tool, this is reasonably complete.

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 every parameter described. The main description does not add extra parameter semantics beyond the schema, and the schema adequately explains each parameter's meaning and defaults.

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 clearly states a specific action ('Run a policy-rate spread what-if scenario'), the resource ('FX pair'), and the outcome ('estimate directional spot impact'). It distinguishes itself from sibling tools like fx_backtest_task and event_impact_replay_task by emphasizing the policy-rate spread heuristic and elasticity assumption.

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 usage for task-augmented requests but does not explicitly contrast with alternative tools. It mentions async support via MCP Tasks, which is helpful context, but lacks explicit guidance on when to choose this tool over siblings.

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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