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get_uk_fca_coverage

Read-only

Use when assessing FCA model risk management compliance readiness or benchmarking an AI governance program against UK regulatory expectations. Returns coverage across 13 control objectives from FCA Policy Statement PS7/24. Example: PS7/24 requires documented model validation methodology, ongoing performance monitoring, and board-level model risk appetite statement — gaps in any of the three trigger supervisory concern. Source: FCA Policy Statement PS7/24. $0.50 USDC per call.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nistFunctionNo

Schema Changelog

Changes observed during successful MCP inspections.

  1. Added

TDQS

A3.6/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false. The description adds useful behavioral detail beyond that: it returns coverage across 13 control objectives, illustrates what a gap analysis entails, names the source regulation, and discloses the per-call price. 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is efficient and front-loaded with the use case, then moves to return value, example, source, and cost. The example sentence is substantive rather than filler, and no sentence is redundant.

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?

The description gives strong context about purpose, source, and output shape, and the annotations cover safety. However, it omits any guidance on the nistFunction parameter and does not differentiate from closely related regulatory coverage tools, leaving some ambiguity for an agent deciding exactly how to call it.

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?

There is one optional nistFunction enum parameter, but the description never mentions it or explains how selecting GOVERN, MAP, MEASURE, or MANAGE affects the returned coverage. With schema description coverage at 0%, the description needed to compensate but did not.

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 a specific verb and resource: it 'Returns coverage across 13 control objectives from FCA Policy Statement PS7/24' for UK regulatory compliance readiness. It does not explicitly differentiate from sibling tools like get_model_risk_management_standards or get_eu_ai_act_coverage, but the FCA/UK specificity makes the resource clear.

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?

It gives explicit context: 'Use when assessing FCA model risk management compliance readiness or benchmarking an AI governance program against UK regulatory expectations.' This provides clear when-to-use direction, though it does not explicitly mention when not to use it or name alternatives.

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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