# executive_comp_peer_benchmark

As a Chief Human Resources Officer (CHRO), benchmark executive compensation packages against peer companies using public SEC filings and private compensation data from Equilar and Bloomberg. Inputs include executive name, title, company ticker, and peer group criteria. Outputs structured compensation metrics (base salary, bonus, equity, total compensation) with source attribution and confidence scores.

Agent View of the PolicyLayer registry record for `executive_comp_peer_benchmark`. HTML page: https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/executive-comp-peer-benchmark

## Facts

- Tool: `executive_comp_peer_benchmark`
- 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: Read (Low risk)
- Registry record: grade F, identity unverified
- Server auth posture: open
- Server rate-limited: no
- Parameters: 6 (3 required)
- Recommended policy verdict: Allowed

## 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 |
| `peerGroup` | object | no |  |
| `fiscalYear` | number | no |  |
| `companyTicker` | string | yes |  |
| `executiveName` | string | yes |  |
| `executiveTitle` | string | 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": "executive_comp_peer_benchmark",
    "arguments": {
      "companyTicker": "<companyTicker>",
      "executiveName": "<executiveName>",
      "executiveTitle": "<executiveTitle>"
    }
  }
}
```

## Why executive_comp_peer_benchmark is rated Low

This tool retrieves and aggregates compensation data from external sources (SEC filings, Equilar, Bloomberg) to produce benchmarking reports. It reads and analyzes existing data without creating, modifying, or deleting anything. Severity is medium because it accesses potentially sensitive compensation data about named executives, raising privacy considerations if misused.

From the tool's own definition: "benchmark executive compensation packages against peer companies using public SEC filings and private compensation data from Equilar and Bloomberg..."

Risk signals: High parameter count (10 properties)

## Use case

AI agents call executive_comp_peer_benchmark 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.

## Recommended policy (PolicyLayer)

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

```json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "executive_comp_peer_benchmark": {}
  }
}
```

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## 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