mcpbeat

ModelRisk MCP Server

io.github.vosesoftware/modelrisk-mcp
local only

ModelRisk runs on your own machine — the client starts it, so there is no endpoint to ping. 785 installs a week from pypi.

Read, build, fit, and run Monte Carlo risk models in Excel through Vose Software's ModelRisk.

Installs per day peak 883 · avg 182 · -23% w/w
a month agotoday
785
Installs / week
pypi · modelrisk-mcp
Stars
on GitHub
Last commit
0 releases in 90 days
License
language unknown

Connect this server

This server runs on your own machine — install it with the package manager and the client starts it for you. Package name taken from the official registry entry.

run in your terminal
claude mcp add modelrisk-mcp -- uvx modelrisk-mcp
~/Library/Application Support/Claude/claude_desktop_config.json
{
  "mcpServers": {
    "modelrisk-mcp": {
      "args": [
        "modelrisk-mcp"
      ],
      "command": "uvx"
    }
  }
}
~/.codex/config.toml
[mcp_servers.modelrisk-mcp]
command = "uvx"
args = ["modelrisk-mcp"]
.cursor/mcp.json
{
  "mcpServers": {
    "modelrisk-mcp": {
      "args": [
        "modelrisk-mcp"
      ],
      "command": "uvx"
    }
  }
}
.vscode/mcp.json
{
  "mcpServers": {
    "modelrisk-mcp": {
      "args": [
        "modelrisk-mcp"
      ],
      "command": "uvx"
    }
  }
}

ModelRisk — questions

Answers built from our own checks of this server.

Why is there no uptime for ModelRisk?
ModelRisk runs on your own machine over stdio — there is no network address to reach, so uptime cannot be measured for it by anyone. What can be measured is adoption: the pypi package modelrisk-mcp was installed 785 times last week.
How do I connect ModelRisk?
Copy the ready config from this page — we generate it for Claude Code, Claude Desktop, Codex, Cursor and VS Code, each with the file path that client actually reads. It runs locally, so the command pulls modelrisk-mcp straight from pypi; nothing to host, nothing to sign up for.
How many people use ModelRisk?
The pypi package modelrisk-mcp was installed 785 times in the last week. Week over week that is -23%. We show installs rather than GitHub stars on purpose: a star is a bookmark, an install is someone actually running it.