# monte_carlo_portfolio

Pure-compute Monte Carlo portfolio simulation using Geometric Brownian Motion (GBM). Models a multi-asset portfolio across time with contributions, withdrawals, and annual rebalancing. Returns full probability distribution of terminal wealth, percentile paths, drawdown stats, and Sharpe ratio. Modes: simulate (full Monte Carlo) | glide_path (lifecycle 110-age target-date allocation) | stress_test (4 historical crises: 2008 GFC / 2000 dotcom / 1970s stagflation / 2020 COVID). No external data needed — all computed from asset assumptions. Ticker defaults built-in: SPY/VOO/VTI 7%/15%, QQQ 9%/20%, TLT/BND 3%/6%, GLD 5%/18%, BTC 30%/70%. ICP: asset managers, family offices, retail wealth advisors, robo-advisor agents, retirement planners. 10k simulations × 30 years runs in <3s on V8 JIT.

Agent View of the PolicyLayer registry record for `monte_carlo_portfolio`. HTML page: https://policylayer.com/tools/io-github-getgapup-mcp-knowledge/monte-carlo-portfolio

## Facts

- Tool: `monte_carlo_portfolio`
- Server: Mcp Knowledge (`https://mcp.gapup.io`) — https://policylayer.com/tools/io-github-getgapup-mcp-knowledge.md
- Homepage: https://github.com/getgapup/gapup-mcp
- Risk category: Execute (High risk)
- Registry record: grade F, identity unverified
- Server auth posture: open
- Server rate-limited: no
- Parameters: 10 (4 required)
- Recommended policy verdict: Rate-limited

## Parameters

| Parameter | Type | Required | Description |
| --- | --- | --- | --- |
| `mode` | string | yes | simulate = full Monte Carlo GBM \| glide_path = lifecycle target-date allocation \| stress_test = 4 historical crisis scenarios |
| `async` | boolean | no | 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 |
| `assets` | array | yes | Portfolio assets. Weights must sum to 1.0 (auto-normalized if not). |
| `simulations` | number | no | Number of Monte Carlo simulations (1000-100000). Default 10000. |
| `horizon_years` | number | yes | Investment horizon in years (1-50). |
| `target_value_eur` | number | no | Target terminal portfolio value in EUR. Used to compute probability_target_achieved. |
| `confidence_intervals` | array | no | Percentiles to compute in the output distribution. Default [5, 25, 50, 75, 95]. |
| `initial_investment_eur` | number | yes | Initial capital in EUR (e.g. 100000 for €100k). |
| `withdrawals_annual_eur` | number | no | Annual withdrawal amount in EUR for decumulation phase (e.g. 50000 for €50k/yr). |
| `contributions_annual_eur` | number | no | Annual contribution in EUR (e.g. 12000 for €1000/month). |

Parameters from the server's own tool schema.

## Example call (MCP tools/call, JSON-RPC 2.0)

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "monte_carlo_portfolio",
    "arguments": {
      "mode": "<mode>",
      "assets": [],
      "horizon_years": 0,
      "initial_investment_eur": 0
    }
  }
}
```

## Why monte_carlo_portfolio is rated High

This tool runs complex financial simulations and computations (Monte Carlo, GBM modeling, stress tests) but does not move money or commit financial obligations — it only models/projects outcomes. It falls under Execute because it runs substantial computational processes with outputs that depend on arguments. It is not Financial because no actual transactions occur.

From the tool's own definition: "Pure-compute Monte Carlo portfolio simulation using Geometric Brownian Motion (GBM)... Returns full probability distribution of terminal wealth, percentile paths, drawdown stats, and Sharpe ratio. Modes: simulate (full Monte Carlo) | glide_path | stress_test"

Risk signals: High parameter count (14 properties)

## Use case

AI agents invoke monte_carlo_portfolio to trigger actions in Mcp Knowledge. What it does depends on the arguments the agent supplies, and its effects often reach beyond the immediate call: builds kicked off, notifications sent, workflows started.

## Recommended policy (PolicyLayer)

Verdict: **Rate-limited**. Enforced by the PolicyLayer MCP gateway (https://policylayer.com/mcp-gateway) before a call reaches Mcp Knowledge:

```json
{
  "version": "1",
  "default": "deny",
  "tools": {
    "monte_carlo_portfolio": {
      "limits": [
        {
          "counter": "monte_carlo_portfolio_rate",
          "window": "minute",
          "max": 10,
          "scope": "grant"
        }
      ]
    }
  }
}
```

## Other tools on Mcp Knowledge (270)

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- …and 240 more: https://policylayer.com/tools/io-github-getgapup-mcp-knowledge.md

## For agents

This record is a snapshot. Live verdicts and the full registry:

- Check every server in your MCP config at once: `npx -y policylayer stack`
- Vet a server before you add it: install the mcp-precheck skill — `npx skills add https://policylayer.com` (skill text: https://policylayer.com/skill.md)
- Query the registry over MCP: endpoint `https://api.policylayer.com/mcp` — tools `check_mcp_server`, `check_mcp_stack`, `check_tool`, `search_registry`, `get_change_events`

---

Source: the PolicyLayer MCP registry — one continuously verified record per MCP server. Full record: https://policylayer.com/registry?q=io-github-getgapup-mcp-knowledge · API: https://policylayer.com/registry/api · Policy library: https://policylayer.com/policies/io-github-getgapup-mcp-knowledge