Statec Lu vs Stata MCP
A side-by-side look at the Statec Lu 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 Statec Lu for data science tools work as a local process; 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 Statec Lu when
- You need dedicated capabilities in the Data Science Tools domain.
- You prefer a local stdio subprocess transport.
- You want tools like
list_dataflows, dataflow_structure, get_data.
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 | 59/100 · Good | 57/100 · Good |
| Install command | Confirmed | Confirmed |
| Transport | Local subprocess (stdio) | 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 | 0 | 267 |
| Directory activity | 1 views 0 installs 0 upvotes | 7 views 0 installs 0 upvotes |
| Tools | 3 | 4 |
| Full listing | View Statec Lu | View Stata MCP |
Tools & capabilities
Statec Lu tools 3
list_dataflowsBrowse or keyword-search STATEC (Luxembourg statistics) datasets, called "dataflows". Each result has an `id` (e.g. "DF_A1100", the dataflowRef you pass to get_data / dataflow_structure) and an English name plus a short description (publication date, periodicity, author, category). STATEC publishes hundreds of datasets, so pass `query` to filter unless you really want the whole catalog. Example: list_dataflows({ query: "population" }) or list_dataflows({ query: "unemployment" }).dataflow_structureGet the structure (Data Structure Definition) of one STATEC dataset: its ordered dimensions and, for each, the valid codes. Use this BEFORE get_data to learn how to build the dot-separated SDMX `key`. The key has one position per dimension, in `dimension_order`; an empty position is a wildcard. Example: dataflow_structure({ dataflow_id: "DF_A1100" }).get_dataPull observations from a STATEC dataset. `key` is a dot-separated SDMX dimension filter, one position per dimension in the order given by dataflow_structure; leave a position empty to wildcard it. Fetch dataflow_structure first to know the dimension order and valid codes. Example: get_data({ dataflow_id: "DF_A1100", key: "Valeur..A", start_period: "2010", end_period: "2020" }) picks VARIABLE=Valeur, wildcards SPECIFICATION, FREQ=A (annual). Omit `key` (or pass "") to fetch all series — caution, this can be large. Returns decoded series with their dimension labels and per-period values.
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.
Statec Lu
{
"mcpServers": {
"statec-lu": {
"command": "npx",
"args": [
"-y",
"@pipeworx/mcp-statec-lu"
]
}
}
}
Stata MCP
{
"mcpServers": {
"stata-mcp": {
"command": "uvx",
"args": [
"stata-mcp"
]
}
}
}
Frequently asked questions
Statec Lu is categorized under Data Science Tools and uses a local stdio subprocess. Stata MCP belongs to Data Science Tools and uses a local stdio subprocess. Pick Statec Lu 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.
Statec Lu 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 statec-lu and stata-mcp as separate keys in the same mcpServers object, and your assistant will call tools from either one.
On AllMCPs, Statec Lu has a quality signal of 59/100 (Good) with 1 views, 0 installs and 0 GitHub stars. Stata MCP has 57/100 (Good) with 7 views, 0 installs and 267 GitHub stars.