mcp-knowledge

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

A3.5/5.0

Behavior4/5

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

Annotations provide readOnlyHint, openWorldHint, and idempotentHint. The description adds context: the tool is asynchronous, generates actionable recommendations, and uses specific data sources to produce ESG-aligned adjustments with sustainability scores. It does not contradict annotations, and the readOnlyHint is consistent with returning recommendations without 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.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is a single, clear paragraph of five sentences, front-loading the purpose and key details. It is concise without redundancy, but could be more structured (e.g., bullet points) for scanability. Overall efficient and well-organized.

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 but indicated), the description does not need to detail return values. It covers inputs, data sources, async behavior, and output type (recommendations with impact scores). It could mention polling for results (job_result), but with the async parameter and sibling job_result tool, this is sufficient.

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 high (80%), so the schema already documents most parameters. The description adds slight value by naming the required inputs (company location, industry, current benefits) and implying the async parameter's necessity. However, it does not elaborate on the esgFocus array or the structure of currentBenefits beyond what the schema provides.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the verb 'align' and resource 'employee benefits packages' with ESG goals, naming specific data sources (Eurostat, MSCI, Sustainalytics). It also identifies the target user (CHROs). However, it does not explicitly distinguish this tool from many sibling ESG tools (e.g., esg_audit_multi, procurement_okr_esg_aligner), which limits differentiation.

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

Usage Guidelines2/5

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

The description notes that the tool is asynchronous and requires async:true to avoid timeouts, providing basic usage guidance. However, it offers no guidance on when to use this tool versus alternatives (e.g., other ESG or benefits tools), and lacks when-not-to-use conditions or prerequisites beyond the inputs listed.

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.