Simulate Monte Carlo vs Stata MCP
A side-by-side look at the Simulate Monte Carlo and Stata MCP MCP servers: transport, auth, tools, quality signals, and the config to install either one in Claude, Cursor, Windsurf or VS Code.
Verdict: choose Simulate Monte Carlo for data science tools work over a hosted endpoint; choose Stata MCP for data science tools as a local process. You can also run both side by side in one mcpServers block.
Which MCP server should you choose?
Choose Simulate Monte Carlo when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a remote HTTP/SSE transport.
Choose Stata MCP when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
- Its auth model fits you: No auth required.
- You want tools like
stata_do, get_data_info, help.
Feature & specification comparison
Rows where the two servers differ are marked.
| Spec | ||
|---|---|---|
| Category | Data Science Tools | Data Science Tools |
| Quality signal | 39/100 · Fair | 57/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Remote HTTP/SSE | Local subprocess (stdio) |
| Auth | Not declared | No auth required |
| Pricing | Not declared | Free |
| Required env vars | None listed | None listed |
| Verified owner | Community listing | Community listing |
| GitHub stars | 1 | 269 |
| Directory activity | 1 views 0 installs 0 upvotes | 7 views 0 installs 0 upvotes |
| Tools | Not listed | 4 |
| Full listing | View Simulate Monte Carlo | View Stata MCP |
Tools & capabilities
Simulate Monte Carlo tools
No tool names declared yet. Check the project README from the listing page.
Stata MCP tools 4
stata_doExecute a Stata do-file and return the execution log. Accepts a do-file path, runs it via the configured Stata executable, and can optionally read log content only when return-code errors are detected. Use when you need to run Stata commands, perform regression or statistical analysis, or execute a do-file.get_data_infoGet descriptive statistics and a data preview for a supported data file (.dta, .csv, .tsv, .psv, .xlsx, .xls, .sav, .zsav). Returns overview, variable details, and optional head rows filtered by requested variables. Use when you need to understand a dataset or have no prior knowledge of the data.helpRetrieve documentation and usage information for a Stata command. Use when you need to understand a command's syntax, options, or troubleshoot errors before running it.read_logRead a Stata log file (.log or .smcl) and return its content. Supports full, core, and dict output formats. Use `lines` to return only the first/last N lines.
Ready-to-paste client configs
Paste either block (or both) into your client config, e.g. claude_desktop_config.json or ~/.cursor/mcp.json. We only show a config when the install command is confirmed.
Simulate Monte Carlo
{
"mcpServers": {
"simulate-monte-carlo": {
"url": "http://localhost:8787/mcp"
}
}
}
Stata MCP
{
"mcpServers": {
"stata-mcp": {
"command": "uvx",
"args": [
"stata-mcp"
]
}
}
}
Frequently asked questions
Simulate Monte Carlo is categorized under Data Science Tools and uses a remote HTTP/SSE transport. Stata MCP belongs to Data Science Tools and uses a local stdio subprocess. Pick Simulate Monte Carlo for data science tools work and Stata MCP for data science tools.
Copy the mcpServers block from the config section on this page into your client's config (for example claude_desktop_config.json or ~/.cursor/mcp.json), then fully restart the client.
Simulate Monte Carlo has no declared pricing — check the project's own site. Stata MCP is listed as free. Auth: no auth required. If a server needs environment variables such as API keys, define them under the env key in your client's MCP config.
Yes. MCP clients support multiple servers: add simulate-monte-carlo and stata-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Simulate Monte Carlo has a quality signal of 39/100 (Fair) with 1 views, 0 installs and 1 GitHub stars. Stata MCP has 57/100 (Good) with 7 views, 0 installs and 269 GitHub stars.