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gapup-mcp

by io.github.getgapup

lgpd_data_subject_rights_automator

Read-onlyIdempotent

Automates LGPD Data Subject Access Requests (DSARs) for legal teams, handling Brazil-specific data retention, erasure, and access workflows. Accepts user identifiers, request type (access/rectification/deletion), and optional scope filters. Returns structured response with compliance status, warnings, and source references to Brazilian LGPD and CNIL decisions.

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.
scopeNoOptional list of data categories to limit the request
urgencyNoPriority level for processing
requestTypeYesType of LGPD request
userIdentifierYesCPF, email, or other unique identifier for the data subject

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
warningsNo
dataCategoriesNo
erasureDeadlineNo
complianceStatusNo
retentionPeriodDaysNo

TDQS

A3.7/5.0
Behavior3/5

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

The description adds context about returning structured responses with compliance status, warnings, and source references, which goes beyond annotations. However, it does not clarify async/job_id behavior or the apparent contradiction between 'erasure handling' and readOnlyHint, though this is not a direct contradiction. The mention of 'CNIL decisions' is odd for a Brazil-specific tool.

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 two concise sentences, with the primary purpose front-loaded. Every sentence adds value without redundancy, making it easy for an agent to scan.

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?

It covers the main purpose, inputs, and output summary, but omits the behavior of the async parameter (job_id polling) and includes a potentially misleading reference to CNIL. Since an output schema exists, return details are covered, but the description still has notable gaps in operational context.

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% and descriptions already cover parameters like userIdentifier and requestType. The description merely restates what the schema provides, adding no extra meaning or format details beyond what is already structured.

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 it automates LGPD Data Subject Access Requests (DSARs) for legal teams, specifying Brazil-specific workflows for retention, erasure, and access. It lists input types and request types, making its scope distinct from general privacy compliance tools.

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

Usage Guidelines3/5

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

Usage is implied for handling LGPD DSARs, but there is no explicit guidance on when to use it versus alternative tools or any exclusions. The description does not mention competing privacy compliance tools or indicate when not to use it.

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

C2.4/5.0
Disambiguation1/5

Over 50 tools share the identical template 'Gapup agent-payable C-suite expertise' with similar French descriptions and reference cases, making their boundaries indistinguishable. Clusters like competitor_intel, competitive_deep_dive, competitor_moves, competitor_profiles, competitor_pricing_radar, competitor_pricing_scrape, and competitor_recommendations heavily overlap in purpose.

Naming Consistency1/5

Names are chaotic: mix of French and English, snake_case and camelCase, verb_noun, noun, and adjective forms with no uniform pattern. Examples like 'bp_narratif', 'content_enrichment', 'ai_governance_full_report_async', and 'job_result' show no coherent naming convention.

Tool Count1/5

271 tools is far beyond any reasonable MCP server scope, creating an overwhelming selection burden for agents. This count vastly exceeds the 25+ threshold for 'too many' and makes navigation impractical.

Completeness2/5

While the server covers many business domains, it lacks lifecycle operations (e.g., no update/delete tools for the deliverables it generates) and the input specifications are vague ('documented case fields' without documentation), creating functional dead ends. The sheer breadth does not compensate for these gaps.

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