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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.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nistFunctionNo

TDQS

A4/5.0
Behavior4/5

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

Annotations already declare readOnlyHint=true and destructiveHint=false, and the description adds useful behavioral context beyond that: it returns coverage across 13 specific control objectives and includes an example of PS7/24 requirements. This gives the agent a sense of what content to expect without contradicting 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 three sentences and front-loaded with the 'Use when' directive. The example sentence adds concrete value, but the final 'Source: FCA Policy Statement PS7/24' is somewhat redundant since the second sentence already mentions PS7/24. Still, overall it is economically written.

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 tool's simplicity (one optional param, no output schema), the description provides adequate context: regulatory source, number of control objectives, and a concrete example. It doesn't explain return format, but without an output schema, that may not be strictly necessary for a coverage-lookup tool. The missing parameter explanation slightly reduces completeness, but the core use case is well covered.

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?

The input schema has one optional parameter (nistFunction) with enum values, but schema description coverage is 0% and the description does not explain this parameter at all. The agent is left guessing what nistFunction means in the context of an FCA tool, how it filters the coverage, or why NIST functions are relevant to a UK FCA assessment. This is a significant gap.

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 states a specific verb ('Returns coverage') and a precise resource ('13 control objectives from FCA Policy Statement PS7/24'), clearly distinguishing it from sibling tools targeting other jurisdictions/regulations. The use case context ('assessing FCA model risk management compliance readiness') further clarifies its scope.

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 explicitly says 'Use when assessing FCA model risk management compliance readiness...' which provides a clear when-to-use signal. However, it does not explicitly mention when not to use it or contrast with alternatives like the EU AI Act tool, though the naming and purpose make the distinction fairly obvious.

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

A4.1/5.0
Disambiguation5/5

Every tool targets a distinct regulatory domain or data source—from OFAC sanctions to CRA ratings to NIST AI RMF—with clear boundaries. Even the three screening tools (OFAC, OIG, SAM) differ by governing agency and list, and their descriptions explicitly disambiguate them.

Naming Consistency5/5

All tools follow the consistent lower_snake_case pattern 'get_<domain>_<focus>', such as get_ofac_sanctions_screening and get_us_state_ai_legislation. There are no mixed conventions, vague verbs, or unexpected abbreviations.

Tool Count4/5

18 tools is slightly above the ideal 3-15 range but appropriate for a broad governance data server covering federal, state, and international regulatory sources. Each tool corresponds to a meaningful dataset, so the count feels justified rather than padded.

Completeness4/5

The surface covers a wide array of governance and compliance domains, including AI regulation, financial enforcement, sanctions, and legal screening, with no critical dead ends for typical lookups. However, it lacks some common regulatory areas (e.g., SEC, HIPAA, GDPR) and offers only read-only access, which is acceptable but not exhaustive.

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