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gapup-mcp

by io.github.getgapup

dpdp_consent_artifact_generator

Read-onlyIdempotent

Generates structured consent artifacts compliant with India's Digital Personal Data Protection Act (DPDP). Designed for legal teams to verify or create consent records with timestamped logs, purpose limitation, and data subject rights. Accepts data subject details, processing purpose, and legal basis as inputs. Returns a signed artifact with audit trail and validation status.

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.
legalBasisYesLegal basis for processing under DPDP
dataSubjectIdYesUnique identifier for the data subject
dataCategoriesNoCategories of personal data being processed
processingPurposeYesSpecific purpose for data processing
retentionPeriodDaysNoRetention period in days

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
sourcesNo
artifactNo
warningsNo

TDQS

A4.2/5.0
Behavior4/5

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

Beyond the readOnlyHint, openWorldHint, and idempotentHint annotations, the description adds useful behavioral details about the output: timestamped logs, purpose limitation, data subject rights, and a signed artifact with audit trail and validation status. The word 'create' introduces slight ambiguity but does not rise to a 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?

Three sentences, front-loaded with purpose. Every sentence adds value: legal context, usage, inputs, and output. There is no redundant filler.

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?

With an output schema and 100% parameter coverage, the description is largely complete. It covers purpose, use case, inputs, and output. It does not mention the async option, but that is already documented in the input schema.

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 description coverage is 100%, so the baseline is 3. The description mentions data subject details, processing purpose, and legal basis, but adds no additional semantics beyond the schema. It does not explain enum options or other 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 generates structured consent artifacts under India's DPDP Act, with a specific verb 'generates' and a clear resource. It distinguishes itself from sibling tools by focusing on DPDP consent artifacts, a unique niche.

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: designed for legal teams to verify or create consent records. It implies when to use but does not name alternatives or exclusions. This aligns with 'clear context, no exclusions'.

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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