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FX Volatility Leaders

fx_volatility_leaders
Read-onlyIdempotent

Pairs ranked by annualized realized volatility. Distinct from fx_movers — surfaces pairs that swing wildly day-to-day, even if flat overall. Classes: low/moderate/elevated/extreme.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
top_nNo
basketNomajors
horizon_daysNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4/5.0
Behavior4/5

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

Annotations already cover read-only/idempotent/non-destructive behavior. The description adds useful behavioral context beyond that: it explains the metric (annualized realized volatility), the 'flat overall' edge case, and the low/moderate/elevated/extreme classification. No contradiction with annotations.

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?

Three compact sentences, front-loaded with the core ranking purpose, followed by the sibling distinction and output classification. Every sentence contributes meaningful information with no filler or repetition.

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 no output schema, the description should clarify return shape, but it only partially does so via the classification classes. It does not describe whether each result includes the volatility value, class label, or pair identifiers, nor does it connect horizon_days or basket to the results. Adequate for a simple read-only ranked list, but with gaps.

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

Parameters2/5

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

Schema description coverage is 0%, and the description adds no explanation for top_n, basket, or horizon_days. While the parameter names and schema defaults are somewhat self-explanatory, 'basket' (g10 vs majors) is left ambiguous and the description does not compensate for the complete lack of parameter documentation.

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 specific verb and resource ('Pairs ranked by annualized realized volatility') and immediately differentiates the tool from fx_movers by explaining it surfaces pairs with high day-to-day swings even if flat overall. It also names the output classification scheme, so an agent knows what to expect.

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 names the closest sibling, fx_movers, and gives the distinguishing criterion: volatility leaders are about daily swing magnitude, while movers are about net movement. This provides clear selection context, though it stops short of an explicit when-to-use/when-not-to-use formulation.

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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