operational_dashboards

Dashboards opérationnels — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Qonto (5 départements · 12 KPIs) — 4 dashboards live en 3 semaines · time-to-décision -55%. Inputs are validated server-side — send the documented case fields.

SERVERMcp Knowledge SOURCEhttps://mcp.gapup.io
Low RISK CLASS
Category Read
Parameters 64 required
Recommended Allowedsee the rule below
Registry record Grade F, identity unverified Pull the record →

This record as markdown: /tools/io-github-getgapup-mcp-knowledge/operational-dashboards.md

What operational_dashboards does on Mcp Knowledge

AI agents call operational_dashboards 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.

ParameterTypeRequiredDescription
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
techStack array Yes
departments array Yes
kpiRequests array Yes
primaryDashboardTool string

Parameters from the server's own tool schema.

Why operational_dashboards is rated Low

The tool appears to generate operational dashboard deliverables (structured reports/analytics) for C-suite (COO) use. It returns structured, audited output based on validated inputs. There is no clear indication of write, execute, destructive, or financial operations — it reads/computes and returns a structured report.

From the tool's definition 'Dashboards opérationnels' and 'Returns a structured, audited deliverable' — the tool generates and returns dashboard/reporting content based on inputs

Risk signalsHigh parameter count (12 properties)

Questions about operational_dashboards

What does the operational_dashboards tool do? +

Dashboards opérationnels — Gapup agent-payable C-suite expertise (COO). Returns a structured, audited deliverable. Reference case: Qonto (5 départements · 12 KPIs) — 4 dashboards live en 3 semaines · time-to-décision -55%. 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.

What parameters does operational_dashboards accept? +

operational_dashboards accepts 6 parameters: async, company, techStack, departments, kpiRequests, primaryDashboardTool. Required: company, techStack, departments, kpiRequests. The full parameter table on this page comes from the server's own tool schema.

How do I enforce a policy on operational_dashboards? +

Register the Mcp Knowledge MCP server in PolicyLayer and add a rule for operational_dashboards: 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.

What risk level is operational_dashboards? +

operational_dashboards is a Read tool with low risk. Read-only tools are generally safe to allow by default.

Can I rate-limit operational_dashboards? +

Yes. Add a rate_limit block to the operational_dashboards 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.

How do I block operational_dashboards completely? +

Set action: deny in the PolicyLayer policy for operational_dashboards. 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.

What MCP server provides operational_dashboards? +

operational_dashboards 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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