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mcp-knowledge

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

C2.9/5.0
Behavior1/5

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

The description claims the tool handles erasure and deletion workflows, which are write operations, but the annotations set readOnlyHint: true, indicating it is read-only. This is a direct contradiction. Additionally, idempotentHint: true is inconsistent with deletion operations. The description fails to disclose this contradiction or explain the actual behavior.

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 two sentences, concise and front-loaded with the core purpose. However, the first sentence could be slightly trimmed without losing meaning. Overall, it is efficiently structured.

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?

Despite an output schema existing, the description mentions return value elements (compliance status, warnings, source references). However, it omits mention of the async parameter, which is critical for processing speed. The annotation contradiction further undermines trust in the described behavior. For a tool with 5 parameters and 2 enums, the description is incomplete.

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% (all parameters described in schema). The description adds context by listing request types and optional scope filters, but does not provide substantial new meaning beyond the schema. Enum values like requestType are already clear from schema. Baseline 3 is appropriate.

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 automates LGPD DSARs, specifies it handles access, rectification, and deletion workflows, and mentions returning a structured response with compliance status, warnings, and source references. This provides a specific verb, resource, and scope, distinguishing it from sibling tools focused on other regulations or general compliance.

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 does not provide explicit guidance on when to use this tool versus alternatives, nor does it mention conditions for non-use or prerequisites. It only implies usage for legal teams handling LGPD DSARs, which is insufficient for an agent to decide between this and similar tools.

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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