enps_auto
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.
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/enps-auto.md
What enps_auto does on Mcp Knowledge
AI agents call enps_auto to retrieve information from Mcp Knowledge without modifying anything. It is typically the context-gathering step in research, monitoring, and reporting workflows, before the agent takes action elsewhere.
| Parameter | Type | Required | Description |
|---|---|---|---|
async | boolean | — | 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 ti |
focus | string | — | |
company | object | Yes | |
context | object | Yes | |
toolStack | object | Yes | |
segmentation | object | Yes | |
presenterScript | array | — |
Parameters from the server's own tool schema.
Why enps_auto is rated Low
The tool appears to generate/return an eNPS (Employee Net Promoter Score) analysis report — a structured deliverable based on provided inputs. It describes reading/analyzing HR survey data and returning insights (segmentation by tenure, manager, corrective plays). There is no clear indication of writing to external systems, executing code, or destructive actions.
From the tool's definition eNPS automatisé — returns a structured, audited deliverable; reference case describes pulse survey segmentation and corrective plays analysis
Risk signalsHigh parameter count (29 properties)
Attacks that exploit this kind of access
The rule that runs enps_auto safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Mcp Knowledge, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For enps_auto, this is the rule to start with:
enps_auto is read-only, so it stays allowed. Everything else on the server is denied unless you say otherwise.
The button opens the PolicyLayer dashboard: create your workspace, connect Mcp Knowledge, apply this rule, and every enps_auto call is checked against it from then on.
Questions about enps_auto
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. It is categorised as a Read tool in the Mcp Knowledge MCP Server, which means it retrieves data without modifying state.
enps_auto accepts 7 parameters: async, focus, company, context, toolStack, segmentation, presenterScript. Required: company, context, toolStack, segmentation. The full parameter table on this page comes from the server's own tool schema.
Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for enps_auto: allow, deny, rate-limit, or require approval. Point your MCP client at the PolicyLayer proxy URL and the rule is enforced on every call, before it reaches Mcp Knowledge. Nothing to install.
enps_auto is a Read tool with low risk. Read-only tools are generally safe to allow by default.
Yes. Add a rate_limit block to the enps_auto rule in your PolicyLayer policy. For example, setting max: 10 and window: 60 limits the tool to 10 calls per minute. Rate limits are tracked per agent session and reset automatically.
Set action: deny in the PolicyLayer policy for enps_auto. The AI agent will receive a policy violation error and cannot call the tool. You can also include a reason field to explain why the tool is blocked.
enps_auto is provided by the Mcp Knowledge MCP server (https://mcp.gapup.io). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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