gapup-mcp

hr_benefits_esg_aligner

Read-onlyIdempotent

Asynchronous tool for Chief Human Resources Officers (CHROs) to align employee benefits packages with ESG (Environmental, Social, Governance) goals. Uses Eurostat HR data, MSCI ESG ratings, and Sustainalytics metrics to generate actionable recommendations. Inputs include company location, industry, and current benefits structure. Outputs ESG-aligned benefits adjustments with sustainability impact scores. Requires async:true to avoid timeout errors.

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.
`esgFocus`	No	Primary ESG pillars to prioritize
`industryCode`	Yes	NACE or ISIC industry classification code
`companyLocation`	Yes	ISO 2-letter country code of company headquarters
`currentBenefits`	Yes

Output Schema

TableJSON Schema

Name	Required	Description	Default
`status`	Yes
`sources`	No
`warnings`	No
`recommendations`	No
`overallESGAlignmentScore`	No

TDQS

A4.3/5.0

Behavior4/5

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

Annotations already declare readOnlyHint, openWorldHint, and idempotentHint, so the safety profile is known. The description adds behavioral transparency by stating it is asynchronous and requires async:true, and it reveals the data sources and output format (sustainability impact scores), which go 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 concise, consisting of four sentences with no redundant information. It front-loads the purpose, then adds data sources, inputs/outputs, and a key async requirement. Every sentence contributes to understanding the tool.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With an existing output schema and 80% schema description coverage, the description provides sufficient context: it states inputs, outputs, data sources, and the async requirement. No critical gaps remain for an agent to select and invoke the tool correctly.

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 80%, so most parameters are already well documented. The description mentions 'company location, industry, and current benefits structure' but does not add meaning beyond the schema. It does not clarify the remaining parameters (like esgFocus) beyond what the schema already states, so a 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 identifies the tool's function: aligning employee benefits packages with ESG goals for CHROs. It specifies the target user, the domain (HR benefits), and distinguishes from generic ESG tools by naming specific data sources (Eurostat, MSCI, Sustainalytics) and outputs (sustainability impact scores).

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 for when to use the tool (CHROs aligning benefits with ESG) and includes a critical usage requirement: 'Requires async:true to avoid timeout errors.' However, it does not explicitly mention alternatives or exclusions, so it does not fully meet the 'explicit when/when-not' bar.

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.