gapup-mcp

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

Name	Required	Description
`async`	No	If 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.
`legalBasis`	Yes	Legal basis for processing under DPDP
`dataSubjectId`	Yes	Unique identifier for the data subject
`dataCategories`	No	Categories of personal data being processed
`processingPurpose`	Yes	Specific purpose for data processing
`retentionPeriodDays`	No	Retention period in days

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`artifact`	No
`warnings`	No

TDQS

A4.2/5.0

Behavior4/5

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

Adds context beyond annotations: mentions timestamped logs, purpose limitation, data subject rights, and return of signed artifact with audit trail and validation status. However, 'Generates' may imply state change, but readOnlyHint true suggests it's harmless. Does not disclose potential errors or side effects.

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. No redundant information. Efficient and easy to scan.

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 presence of an output schema (not shown) and annotations, the description covers key inputs and outputs. Mentioning audit trail and validation status adds depth. Could be improved by clarifying whether it stores the artifact or just generates it.

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 schema already documents all parameters. Description mentions three required parameters (dataSubjectId, processingPurpose, legalBasis) but adds no additional meaning beyond what the schema provides. Baseline score of 3 applies.

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?

Description clearly states it generates structured consent artifacts compliant with India's DPDP. It specifies the domain (legal teams) and key outputs (signed artifact with audit trail). Distinguishes itself from siblings by its niche focus.

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?

Explicitly says it's designed for legal teams to verify or create consent records, giving clear context. Does not provide when-not-to-use or explicit alternatives, but the specificity makes usage clear.

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

C2.5/5.0

Disambiguation2/5

With 271 tools, many have overlapping purposes (e.g., multiple competitor intel tools, multiple financial modelers, multiple ESG auditors). Detailed descriptions help slightly, but the sheer volume creates confusion. Agents would struggle to select the right tool among many similar options.

Naming Consistency1/5

Tool names are wildly inconsistent: mix of English and French, snake_case and short phrases, some very generic (process, run, execute equivalents). No discernible naming convention (e.g., abm_architect vs. boundary_control vs. bp_narratif). This makes it hard to predict tool names.

Tool Count1/5

271 tools is far beyond typical well-scoped servers (3-15). This indicates an unfocused, over-bloated tool surface. Even for a general business intelligence server, this number is excessive and violates the principle of each tool earning its place.

Completeness2/5

Despite the large count, coverage feels scattered. Some domains (e.g., content, competitive intel) have many tools, while others (e.g., supply chain, HR) have gaps. The set lacks a coherent scope; it seems like a dump of many separate tool collections rather than a complete, curated surface.

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