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mcp-knowledge

by io.github.getgapup

privacy_compliance_audit

Read-only

Audit conformité vie privée — Gapup agent-payable C-suite expertise (RISK). Returns a structured, audited deliverable. Reference case: Lemlist SAS — SaaS outreach B2B, transferts UE→US Schrems II, RGPD + CCPA + LGPD + UK GDPR. Inputs are validated server-side — send the documented case fields.

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.
focusNo
companyYes
presenterScriptNo
targetFrameworksYes
processingActivitiesYes

TDQS

B3.1/5.0
Behavior4/5

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

Annotations indicate readOnlyHint=true and openWorldHint=true. The description adds valuable behavioral context: the tool is 'agent-payable' (cost implication) and validates inputs server-side. It also returns a 'structured, audited deliverable,' consistent with read-only nature. 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.

Conciseness3/5

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

The description is relatively short with three sentences, but includes a lengthy, specific reference case that is not generally useful for an AI agent. The mix of French and English may add unnecessary complexity. It could be more concise by omitting the case study.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (nested input schema, no output schema, low schema coverage), the description is incomplete. It does not explain the deliverable's structure, the audit process, or how to populate the complex parameters. The context provided is minimal and insufficient for proper tool invocation.

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

Parameters1/5

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

Schema description coverage is only 17%, yet the description provides no explanation of parameters (company, processingActivities, targetFrameworks, etc.). The vague instruction 'send the documented case fields' fails to add meaning beyond the schema's property names. This is severely inadequate for a tool with nested objects and 6 parameters.

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 tool performs a privacy compliance audit ('Audit conformité vie privée') and returns a structured deliverable. It differentiates from sibling audit tools by specifying privacy focus and mentioning relevant regulations (RGPD, CCPA, LGPD, UK GDPR), making it distinct from ESG or AI governance audits.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description lacks explicit guidance on when to use this tool versus alternatives. It mentions 'C-suite expertise (RISK)' implying high-level risk assessment, but no exclusions or conditions are provided. The reference case is too specific to generalize usage criteria.

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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