# mcs-mcp

> mcs-mcp — bbak-mcs-mcp. Use this tool when you need to perform probabilistic analysis and diagnostics on flow data using Monte-Carlo simulations, providing AI agents with stochastic modeling capabilities to solve uncertainty and risk assessment problems. It takes in flow data and simulation parameters as inputs and outputs diagnostic results and probability distributions. Ideal for use cases involving predictive modeling, risk analysis, and decision-making under uncertainty.

Canonical page: https://skillsregistry.net/skills/bbak-mcs-mcp  
JSON: https://api.skillsregistry.net/v1/skills/bbak-mcs-mcp

## Description

Provide Monte-Carlo-Simulation and Flow Data diagnostics to AI Agents

## Trust

- **Trust score (0–1):** 0.65
- **Verification tier:** scanned
- **Last scanned:** 2026-06-18

## Facts

- **Version:** 1.0.0
- **Skill type:** atomic
- **Execution layer:** container
- **Runtime environment:** vm
- **Category:** ai-ml
- **Updated:** 2026-09-20

## Source

- **Source listing:** [GitHub](https://github.com/bbak/mcs-mcp)

## Use it

Resolve this record through the SkillsRegistry MCP server (no auth, read-only):

```
claude mcp add --transport http --scope user skillsregistry https://api.skillsregistry.net/mcp
```

```json
{
  "jsonrpc": "2.0",
  "id": 1,
  "method": "tools/call",
  "params": {
    "name": "get_skill",
    "arguments": {
      "slug": "bbak-mcs-mcp"
    }
  }
}
```

REST: `GET https://api.skillsregistry.net/v1/skills/bbak-mcs-mcp` · pull for local use: `GET https://api.skillsregistry.net/v1/skills/bbak-mcs-mcp/pull`

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SkillsRegistry indexes agent skills from public registries and GitHub. Skills we have analysed are scanned with Circle-IR and scored on six dimensions; each listing states its scan coverage. More: https://skillsregistry.net/llms.txt