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gapup-mcp

by io.github.getgapup

labor_law_alert_geo

Read-onlyIdempotent

Provides CHROs with daily alerts on new labor law changes by jurisdiction (state/country). Inputs include jurisdiction (ISO country/state code) and optional date range. Outputs structured legislative updates with summaries, effective dates, and source links. Useful for compliance monitoring, risk assessment, and policy adjustments. Keywords: labor law, compliance, legislation, jurisdiction, CHRO, HR policy.

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.
sinceNoOptional start date for changes (YYYY-MM-DD). Defaults to 7 days ago.
untilNoOptional end date for changes (YYYY-MM-DD). Defaults to today.
jurisdictionYesISO 3166-1 alpha-2 country code or ISO 3166-2 state/province code (e.g., 'US-CA', 'FR')

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
statusYes
changesYes
sourcesYes
warningsYes
last_updatedNo

TDQS

A4.2/5.0
Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint. The description adds value by detailing the output structure ('structured legislative updates with summaries, effective dates, and source links') and the nature of the alerts ('daily'). This enriches the behavioral context beyond the annotations.

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 three sentences: first states the purpose, second summarizes inputs and outputs, third lists use cases and keywords. It is front-loaded with the most critical information and contains no fluff.

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?

The description covers the tool's purpose, inputs, outputs, and use cases. It does not explain the async parameter, but that is detailed in the schema. The output schema exists, so return values are well-defined. Minor ambiguity about whether 'daily alerts' implies a push model, but overall it is adequate.

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% with each parameter having a description. The description restates the key inputs (jurisdiction, date range) but does not add significant new semantics beyond the schema, which is already comprehensive.

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 function: 'Provides CHROs with daily alerts on new labor law changes by jurisdiction (state/country).' It specifies the verb (provides alerts), the resource (labor law changes), and the scope (by jurisdiction), distinguishing it from sibling tools.

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 says it is 'Useful for compliance monitoring, risk assessment, and policy adjustments,' giving clear context for when to use it. It does not explicitly name alternatives or exclusions, but the context is sufficient given the tool's niche.

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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