budget_variance_ai
Analyse d'écart budgétaire — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Explain the key drivers of the budget vs actual variance for <company> in <period> — what are the top 10 narrative explanations? · Which cost categories drove the budget o...
This record as markdown: /tools/io-github-getgapup-mcp-knowledge/budget-variance-ai.md
What budget_variance_ai does on Mcp Knowledge
AI agents call budget_variance_ai 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 |
focus | string | — | |
entity | object | Yes | |
budgetContext | object | Yes |
Parameters from the server's own tool schema.
Why budget_variance_ai is rated Low
This tool performs analytical and advisory work — it reads/analyzes financial data and returns narrative explanations, scenario analyses, and memos. There is no indication it moves money, modifies records, or executes code. It is essentially a CFO-expertise query tool that produces structured analytical output.
From the tool's definition Analyse d'écart budgétaire — Returns a structured, audited deliverable. Answers questions about explaining budget vs actual variance, cost categories, forecast scenarios, and preparing memos.
Risk signalsHigh parameter count (13 properties)
Attacks that exploit this kind of access
The rule that runs budget_variance_ai 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 budget_variance_ai, this is the rule to start with:
budget_variance_ai 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 budget_variance_ai call is checked against it from then on.
Questions about budget_variance_ai
Analyse d'écart budgétaire — Gapup agent-payable C-suite expertise (CFO). Returns a structured, audited deliverable. Answers: Explain the key drivers of the budget vs actual variance for <company> in <period> — what are the top 10 narrative explanations? · Which cost categories drove the budget overrun for <company> in <quarter>, and what corrective actions should management take? · Revise the Q4 forecast based on observed Q3 variances for <company> — give me 3 scenarios (base, optimistic, conservative). · Prepare a board-ready budget variance memo for <company> — <period>, budget €<X>M vs actual €<Y>M, with management actions. · What are the quick wins to reduce budget overspend for <company> by end of quarter without impacting growth targets? Reference case: Doctolib Q3 2026 — budget €38.5M vs actual €41.2M (+7.0%) — cloud + headcount + deals timing. 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.
budget_variance_ai accepts 4 parameters: async, focus, entity, budgetContext. Required: entity, budgetContext. 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 budget_variance_ai: 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.
budget_variance_ai 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 budget_variance_ai 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 budget_variance_ai. 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.
budget_variance_ai 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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