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mcp-knowledge

by io.github.getgapup

enps_auto

Read-only

eNPS automatisé — Gapup agent-payable C-suite expertise (CHRO). Returns a structured, audited deliverable. Reference case: BlaBlaCar — eNPS pulse mensuel · 700 FTE 8 pays · segments × tenure × manager · plays correctifs ciblés. Inputs are validated server-side — send the documented case fields.

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.
focusNo
companyYes
contextYes
toolStackYes
segmentationYes
presenterScriptNo

TDQS

B3.1/5.0
Behavior3/5

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

Annotations indicate readOnlyHint=true, which aligns with the description's 'returns a structured deliverable' (non-mutating). The description adds limited behavioral context (server-side validation) beyond annotations. Score 3.

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 three sentences, including a reference case, and is relatively concise. It could be more structured (e.g., bullet points) but wastes few words. Score 4.

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

Completeness2/5

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

The tool has complex schema (7 parameters, nested objects) and no output schema, yet the description is minimal. It does not explain the deliverable contents, async behavior beyond the async parameter description, or how results are retrieved. The reference case is helpful but not comprehensive. Score 2.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters2/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is only 14% (only the 'async' parameter has a description). The description does not compensate; it only mentions 'send the documented case fields' without detailing parameters. This is insufficient for a tool with 7 parameters including nested objects. Score 2.

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 states the tool returns a structured, audited eNPS deliverable, with a reference case. It is specific enough but does not differentiate from sibling tools such as 'comp_benchmark_geo_delta' or 'talent_intelligence' that might also handle HR metrics. Hence a score of 4.

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 provides a reference case (BlaBlaCar) and mentions inputs are validated server-side, but lacks explicit guidance on when to use or not use this tool compared to alternatives. With many siblings, this is a gap, scoring 3.

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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