# onboarding_salaries

Onboarding opérationnel des salariés — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Pennylane (FR fintech SaaS, ~250 FTE) — 5 parcours 30/60/90 jours · Engineering / Sales / CS / Design / People Ops. Inputs are validated server-side — send the documented case fields.

Agent View of the PolicyLayer registry record for `onboarding_salaries`. HTML page: https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/onboarding-salaries

## Facts

- Tool: `onboarding_salaries`
- Server: Mcp Knowledge (`https://mcp.gapup.io`) — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge.md
- Homepage: https://github.com/getgapup/gapup-mcp
- Risk category: Write (Medium risk)
- Registry record: grade F, identity unverified
- Server auth posture: open
- Server rate-limited: no
- Parameters: 4 (2 required)
- Recommended policy verdict: Rate-limited

## Parameters

| Parameter | Type | Required | Description |
| --- | --- | --- | --- |
| `async` | boolean | 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 ti |
| `focus` | string | no |  |
| `roles` | array | yes |  |
| `company` | object | yes |  |

Parameters from the server's own tool schema.

## Example call (MCP tools/call, JSON-RPC 2.0)

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "onboarding_salaries",
    "arguments": {
      "roles": [],
      "company": {}
    }
  }
}
```

## Why onboarding_salaries is rated Medium

The tool creates or modifies employee onboarding workflows and records (journeys, timelines, structured outputs) for multiple departments (Engineering, Sales, CS, Design, People Ops). This is reversible data creation/modification (Write category). Severity is high because misuse could affect numerous employees' onboarding processes, HR records, and operational workflows at scale.

From the tool's own definition: "Tool description states 'Onboarding opérationnel des salariés' (operational employee onboarding) and mentions 'agent-payable C-suite expertise' with structured deliverables."

Risk signals: High parameter count (17 properties)

## Use case

AI agents use onboarding_salaries to create or update resources in Mcp Knowledge, usually the action step of a workflow, after the agent has gathered context. Every call changes real data in your Mcp Knowledge environment.

## Recommended policy (PolicyLayer)

Verdict: **Rate-limited**. Enforced by the PolicyLayer MCP gateway (https://policylayer.com/mcp-gateway) before a call reaches Mcp Knowledge:

```json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "onboarding_salaries": {
      "limits": [
        {
          "counter": "onboarding_salaries_rate",
          "window": "minute",
          "max": 30,
          "scope": "grant"
        }
      ]
    }
  }
}
```

## Other tools on Mcp Knowledge (270)

- `adversarial_input_stress_tester` — Execute — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/adversarial-input-stress-tester.md
- `competitor_pricing_scrape` — Execute — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/competitor-pricing-scrape.md
- `dora_operational_resilience_stress_tes` — Execute — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/dora-operational-resilience-stress-tes.md
- `financial_model_3statement` — Execute — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/financial-model-3statement.md
- `gl_reconciler` — Execute — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/gl-reconciler.md
- `lgpd_data_subject_rights_automator` — Execute — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/lgpd-data-subject-rights-automator.md
- `monte_carlo_portfolio` — Execute — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/monte-carlo-portfolio.md
- `workflow_orchestrator` — Execute — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/workflow-orchestrator.md
- `ma_arbitrage_hunter` — Financial — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/ma-arbitrage-hunter.md
- `repo_rate_arbitrage_scanner` — Financial — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/repo-rate-arbitrage-scanner.md
- `tax_optimization` — Financial — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/tax-optimization.md
- `treasury_optimizer` — Financial — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/treasury-optimizer.md
- `usdc_x402_payments_intel` — Financial — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/usdc-x402-payments-intel.md
- `working_capital_fx_hedge_optimizer` — Financial — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/working-capital-fx-hedge-optimizer.md
- `pricing_in_deal` — Other — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/pricing-in-deal.md
- `term_sheet_negotiation` — Other — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/term-sheet-negotiation.md
- `abm_architect` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/abm-architect.md
- `abm_lookalike_account_finder` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/abm-lookalike-account-finder.md
- `account_expansion_mapper` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/account-expansion-mapper.md
- `affiliate_fraud_clickstream_detector` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/affiliate-fraud-clickstream-detector.md
- `africa_trade_barrier_breaker` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/africa-trade-barrier-breaker.md
- `africa_trade_finance_esg_rater` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/africa-trade-finance-esg-rater.md
- `africa_trade_preference_arbitrage` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/africa-trade-preference-arbitrage.md
- `africa_trade_preference_optimizer` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/africa-trade-preference-optimizer.md
- `agoa_eba_intelligence` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/agoa-eba-intelligence.md
- `ai_act_sandbox_regulatory_sandbox` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/ai-act-sandbox-regulatory-sandbox.md
- `ai_act_training_data_audit` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/ai-act-training-data-audit.md
- `ai_governance_full_report_async` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/ai-governance-full-report-async.md
- `ai_governance_full_report_result` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/ai-governance-full-report-result.md
- `ai_governance_pilot` — Read — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/ai-governance-pilot.md
- …and 240 more: https://policylayer.com/tools/io-github-getgapup-mcp-knowledge.md

## For agents

This record is a snapshot. Live verdicts and the full registry:

- Check every server in your MCP config at once: `npx -y policylayer stack`
- Vet a server before you add it: install the mcp-precheck skill — `npx skills add https://policylayer.com` (skill text: https://policylayer.com/skill.md)
- Query the registry over MCP: endpoint `https://api.policylayer.com/mcp` — tools `check_mcp_server`, `check_mcp_stack`, `check_tool`, `search_registry`, `get_change_events`

---

Source: the PolicyLayer MCP registry — one continuously verified record per MCP server. Full record: https://policylayer.com/registry?q=io-github-getgapup-mcp-knowledge · API: https://policylayer.com/registry/api · Policy library: https://policylayer.com/policies/io-github-getgapup-mcp-knowledge