budget_variance_ai
shallowio.github.getgapup/mcp-knowledge · Verify this server
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.
1 trials · measured 8 days ago
budget_variance_ai scores 100.0/100 on Vouch's measured behaviour index, from 1 real invocation trials against io.github.getgapup/mcp-knowledge, measured 25 Aug 2026 under methodology v0.2.0. Every measured component scored 100.
Component breakdown
| Component | Weight | Value |
|---|---|---|
| Reliability | 35% | not applicable |
| Schema integrity | 25% | 100.0 |
| Failure behaviour | 15% | not applicable |
| Latency | 15% | not applicable |
| Concurrency | 10% | not applicable |
Tool details
- Transport
- remote
- Credential class
- self-provisionable
- Category
- Content & media
- Input schema
- not declared
- Output schema
- not declared
- Side-effect classification
- unclassified
Score history
| Day | Score | Tier | Methodology |
|---|---|---|---|
| 2026-08-25 | 100.0 | shallow | v0.2.0 |
Probe evidence
| Probe | Outcomes |
|---|---|
| schema_integrity | pass: 1 |
Raw request/response logs are not archived yet — the outcome counts above are drawn directly from every recorded trial.
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