revops_architect
Architecte RevOps — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Qonto — ARR €200M · 200 reps · forecast ±35% · fuite €4,2M/an identifiée · plan RevOps 12 semaines. Inputs are validated server-side — send the documented case fields.
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/revops-architect.md
What revops_architect does on Mcp Knowledge
AI agents call revops_architect 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 |
company | object | Yes | |
keyMetrics | object | Yes | |
objectives | array | Yes | |
revenueTeam | object | Yes | |
currentStack | array | Yes | |
horizonMonths | number | Yes | |
currentPainPoints | array | Yes |
Parameters from the server's own tool schema.
Why revops_architect is rated Low
The tool appears to generate structured analysis/advisory output (RevOps architecture plan, gap analysis) based on input data. This is primarily a read/query-style tool that returns analytical deliverables. No evidence of data mutation, execution of code, deletion, or financial transactions.
From the tool's definition 'Architecte RevOps' returning 'a structured, audited deliverable' based on input case fields — describes analysis and reporting output; reference case shows revenue operations planning and gap identification
Risk signalsHigh parameter count (26 properties)
Attacks that exploit this kind of access
The rule that runs revops_architect 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 revops_architect, this is the rule to start with:
revops_architect 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 revops_architect call is checked against it from then on.
Questions about revops_architect
Architecte RevOps — Gapup agent-payable C-suite expertise (CRO). Returns a structured, audited deliverable. Reference case: Qonto — ARR €200M · 200 reps · forecast ±35% · fuite €4,2M/an identifiée · plan RevOps 12 semaines. 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.
revops_architect accepts 8 parameters: async, company, keyMetrics, objectives, revenueTeam, currentStack, horizonMonths, currentPainPoints. Required: company, keyMetrics, objectives, revenueTeam, currentStack, horizonMonths, currentPainPoints. 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 revops_architect: 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.
revops_architect 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 revops_architect 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 revops_architect. 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.
revops_architect 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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