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mcp-knowledge

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

A3.9/5.0
Behavior3/5

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

Annotations declare readOnlyHint, openWorldHint, and idempotentHint. The description adds that it returns a signed artifact with audit trail, but does not clarify the behavioral implications of 'create' in light of readOnlyHint. It adds some value but does not fully explain traits like state modification or side effects beyond what annotations already convey.

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 concise (three sentences) and front-loads the main purpose. Every sentence contributes meaningful information. Slightly more structured formatting could improve scanability, but overall very efficient.

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 6 parameters, 3 required, and the existence of an output schema (not shown), the description provides sufficient context: domain, user, inputs, and outputs. It explains the artifact's components (timestamped logs, purpose limitation, data subject rights) and mentions validation status. Adequate for the tool's complexity.

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 the schema already describes all parameters. The description briefly mentions inputs (data subject details, processing purpose, legal basis) but does not add new semantics or constraints beyond the 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's purpose: generating structured consent artifacts for India's DPDP Act. It specifies the regulatory framework, target users (legal teams), and output characteristics (signed artifact with audit trail). The DPDP-specific language effectively distinguishes it from sibling tools like lgpd_data_subject_rights_automator.

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. However, it does not explicitly state when to avoid this tool or mention alternatives (e.g., for non-Indian regulations). The context is sufficient but lacks 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

C2.8/5.0
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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