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

A3.9/5.0

Behavior4/5

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

The description adds behavioral context beyond annotations: it is async, uses specific data sources (Eurostat, MSCI, Sustainalytics), and outputs recommendations with scores. This adds value as the annotations only indicate readOnly, openWorld, and idempotent. There is no contradiction between the description and annotations (readOnlyHint is plausible for a recommendation generator).

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 a compact three-sentence paragraph that front-loads purpose, then describes inputs/outputs and a key behavior (async). Every sentence contributes value without redundancy. Slightly more structure could improve scannability, but it is 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 the tool has an output schema (not shown but indicated), the description adequately covers inputs, data sources, and async behavior. It provides enough context for an AI agent to understand when and how to invoke it, though it could mention the output format briefly.

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%, high enough for a baseline of 3. The description mentions the parameters (company location, industry, current benefits) but adds little detail about their meaning beyond what the schema already provides. It does explain the async parameter's purpose, which is helpful. Overall, marginal added value.

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: aligning employee benefits with ESG goals for CHROs. It specifies the verb 'align', the resource 'benefits packages', and the target audience. This distinguishes it from sibling tools like procurement_okr_esg_aligner which focus on procurement.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines3/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

The description mentions the tool is asynchronous and requires async:true to avoid timeouts, and that it uses specific data sources. However, it does not explicitly state when to use this tool versus alternatives, nor does it provide conditions for when not to use it. The guidance is limited to the async behavior.

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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