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 compensa...
This record as markdown: /tools/io-github-getgapup-gapup-mcp/executive-comp-peer-benchmark.md
What executive_comp_peer_benchmark does on Gapup Mcp
AI agents call executive_comp_peer_benchmark to retrieve information from Gapup Mcp 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 |
peerGroup | object | — | |
fiscalYear | number | — | |
companyTicker | string | Yes | |
executiveName | string | Yes | |
executiveTitle | string | Yes |
Parameters from the server's own tool schema.
Why executive_comp_peer_benchmark is rated Low
This tool retrieves and aggregates compensation data from public SEC filings and third-party sources (Equilar, Bloomberg) for benchmarking purposes. It is purely a read/query operation with no write, execute, or financial transaction capabilities. Misuse risk is low — it outputs structured data about executive pay, though there is some sensitivity around competitive intelligence.
From the tool's definition benchmark executive compensation packages against peer companies using public SEC filings and private compensation data... Outputs structured compensation metrics (base salary, bonus, equity, total compensation) with source attribution and confidence scores
Risk signalsHigh parameter count (10 properties)
Attacks that exploit this kind of access
The rule that runs executive_comp_peer_benchmark safely
PolicyLayer is an MCP gateway: it sits between your AI agents and Gapup Mcp, and checks every tool call against a rule you set before the call runs. Nothing changes on the server itself. For executive_comp_peer_benchmark, this is the rule to start with:
executive_comp_peer_benchmark 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 Gapup Mcp, apply this rule, and every executive_comp_peer_benchmark call is checked against it from then on.
Questions about 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. It is categorised as a Read tool in the Gapup Mcp MCP Server, which means it retrieves data without modifying state.
executive_comp_peer_benchmark accepts 6 parameters: async, peerGroup, fiscalYear, companyTicker, executiveName, executiveTitle. Required: companyTicker, executiveName, executiveTitle. The full parameter table on this page comes from the server's own tool schema.
Register the Gapup MCP server in PolicyLayer and add a rule for executive_comp_peer_benchmark: 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 Gapup Mcp. Nothing to install.
executive_comp_peer_benchmark 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 executive_comp_peer_benchmark 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 executive_comp_peer_benchmark. 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.
executive_comp_peer_benchmark is provided by the Gapup MCP server (https://mcp.gapup.io/mcp). PolicyLayer sits as a proxy in front of this server to enforce policies before tool calls reach the server.
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