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mcp-knowledge

by io.github.getgapup

sovereign_data_breach_impact

Read-onlyIdempotent

Estimates financial impact of a data breach across three jurisdictions (US, EU, UK) for CFO strategic planning. Inputs include breach size, industry sector, and affected jurisdictions. Outputs include direct costs, regulatory fines, reputational damage, and cyber insurance premium adjustments. Ideal for cross-border risk assessment, financial contingency planning, and board-level reporting. Keywords: data breach cost, regulatory fines, cyber insurance, financial risk, cross-jurisdiction impact.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
asyncNoIf true, returns a job_id immediately (<200ms) instead of waiting for the result. Poll the result with job_result(job_id). Use for slow tools to avoid client timeouts.
industryNoIndustry sector of the affected organization
records_lostYesNumber of records compromised in the breach
jurisdictionsYesJurisdictions where the breach has legal or financial impact
detection_time_daysNoTime in days to detect the breach

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
total_cost_usdNoEstimated total financial impact in USD
cost_per_record_usdNoCost per compromised record in USD
regulatory_fines_usdNo
cyber_insurance_impactNo
reputational_damage_usdNoEstimated reputational damage cost in USD

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare read-only and idempotent behavior. The description adds useful behavioral context by specifying the types of outputs (direct costs, regulatory fines, reputational damage, cyber insurance premium adjustments) and summarizing key inputs, complementing the annotations without contradiction.

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 three sentences long, front-loading the main purpose and then listing inputs/outputs briefly. Every sentence adds value without redundancy, making it efficient and easy to parse.

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 complexity (5 parameters, output schema present), the description covers the essential context: use cases, key inputs, and output types. It does not explain interpretation of outputs or assumptions, but the output schema likely provides structure. Overall sufficient for practical use.

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 coverage is 100%, so baseline is 3. The description mentions 'breach size, industry sector, and affected jurisdictions' which correspond to schema parameters, but does not add new semantic details beyond summarizing the schema. The async parameter is not mentioned, but this is acceptable given high schema coverage.

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 the verb ('Estimates financial impact') and the specific resource ('data breach across three jurisdictions'). It explicitly identifies the target user (CFO strategic planning) and distinguishes from sibling tools by focusing on financial impact estimation, which is unique among the listed siblings.

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 provides clear context for usage, listing ideal scenarios such as 'cross-border risk assessment, financial contingency planning, and board-level reporting'. However, it does not explicitly state when not to use or compare to alternative tools, missing some exclusion guidance.

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

C2.8/5.0
Disambiguation2/5

Many tools have overlapping purposes, especially in competitive intelligence, ESG, and risk assessment. For example, there are multiple tools for competitor analysis (competitive_deep_dive, competitor_intel, competitor_moves, etc.) with unclear boundaries. Agents would struggle to select the correct tool without deep understanding of subtle differences.

Naming Consistency2/5

Tool names are a mix of English and French, and follow no consistent pattern. Some use snake_case (e.g., abm_architect, action_plan_esg), while others are verb-focused (e.g., content_catalog, fx_rate). The lack of a uniform naming convention makes it hard for agents to predict tool names.

Tool Count1/5

With 271 tools, the server is excessively large. Even for a broad knowledge domain, this number of tools makes discovery and selection inefficient. Typical coherent servers have 3-15 tools; this has an order of magnitude more, indicating poor scoping.

Completeness3/5

The tool set covers many domains (compliance, finance, marketing, HR, etc.), but the coverage is uneven due to redundancy. Key areas have multiple overlapping tools, while some sub-domains may still have gaps. Overall, the surface is broad but not well-curated.

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