Stata MCP Server

A Model Context Protocol (MCP) server that connects AI agents to a local Stata installation.

If you'd like a fully integrated VS Code extension to run Stata code without leaving your IDE, and also allow AI agent interaction, check out my other project: .

Built by Thomas Monk, London School of Economics.

This server enables LLMs to:

• Execute Stata code: run any Stata command (e.g. sysuse auto, regress price mpg).

• Inspect data: retrieve dataset summaries and variable codebooks.

• Export graphics: generate and view Stata graphs (histograms, scatterplots).

• Streaming graph caching: automatically cache graphs during command execution for instant exports.

• Verify results: programmatically check stored results (r(), e()) for accurate validation.

Prerequisites

• Stata 17+ (Stata MP, SE, or BE). Must be licensed and installed locally.

• Python 3.11+

• uv (recommended)

Note on : This server uses the proprietary pystata module that is included with your Stata installation. There is a third-party package named pystata on PyPI that is not the official Stata package and should not be installed. MCP-Stata handles finding and loading the official module from your Stata directory automatically.

Installation

Run as a published tool with uvx

uvx is an alias for uv tool run and runs the tool in an isolated, cached environment.

Configuration

This server attempts to automatically discover your Stata installation (supporting standard paths and StataNow).

If auto-discovery fails, set the STATA_PATH environment variable to your Stata executable:

If you encounter write permission issues with temporary files (common on Windows), you can override the temporary directory location by setting MCP_STATA_TEMP:

The server will automatically try the following locations in order of preference:

1. MCP_STATA_TEMP environment variable

2. System temporary directory

3. ~/.mcp-stata/temp

4. Current working directory subdirectory (.tmp/)

If you prefer, add these variables to your MCP config's env for any IDE shown below. It's optional and only needed when discovery cannot find Stata.

Optional env example (add inside your MCP server entry):

IDE Setup (MCP)

This MCP server uses the stdio transport (the IDE launches the process and communicates over stdin/stdout).

Claude Desktop

Open Claude Desktop → Settings → Developer → Edit Config. Config file locations include:

• macOS: ~/Library/Application Support/Claude/claude_desktop_config.json

• Windows: %APPDATA%\Claude\claude_desktop_config.json

Published tool (uvx)

After editing, fully quit and restart Claude Desktop to reload MCP servers.

Cursor

Cursor supports MCP config at:

• Global: ~/.cursor/mcp.json

• Project: .cursor/mcp.json

Published tool (uvx)

Windsurf

Windsurf supports MCP plugins and also allows manual editing of mcp_config.json. After adding/editing a server, use the UI’s refresh so it re-reads the config.

A common location is ~/.codeium/windsurf/mcp_config.json.

Published tool (uvx)

Google Antigravity

In Antigravity, MCP servers are managed from the MCP store/menu; you can open Manage MCP Servers and then View raw config to edit mcp_config.json.

Published tool (uvx)

Visual Studio Code

VS Code supports MCP servers via a .vscode/mcp.json file. The top-level key is servers (not mcpServers).

Create .vscode/mcp.json:

Published tool (uvx)

VS Code documents .vscode/mcp.json and the servers schema, including type and command/args.

Skills

• Skill file (for Claude/Codex): skill/SKILL.md

Tools Available (from server.py)

• run_command(code, echo=True, as_json=True, trace=False, raw=False, max_output_lines=None, session_id="default"): Execute Stata syntax in the specified session.

  • Always writes output to a temporary log file and emits a single notifications/logMessage containing {"event":"log_path","path":"..."} so the client can tail it locally.

  • May emit notifications/progress when the client provides a progress token/callback.

• read_log(path, offset=0, max_bytes=65536): Read a slice of a previously-provided log file (JSON: path, offset, next_offset, data).

• find_in_log(path, query, start_offset=0, max_bytes=5_000_000, before=2, after=2, case_sensitive=False, regex=False, max_matches=50): Search a log file for text and return context windows.

  • Returns JSON with matches (context lines, line indices), next_offset, and truncated if max_matches is hit.

  • Supports literal or regex search with bounded read window for large logs.

• load_data(source, clear=True, as_json=True, raw=False, max_output_lines=None, session_id="default"): Heuristic loader (sysuse/webuse/use/path/URL) for the specified session.

• get_ui_channel(session_id="default"): Return a short-lived localhost HTTP endpoint + bearer token for the UI-only data browser, targeting the specified session.

• describe(session_id="default"): View dataset structure via Stata describe.

• list_graphs(session_id="default"): See available graphs in memory (JSON list with an active flag).

• export_graph(graph_name=None, format="pdf", session_id="default"): Export a graph to a file path.

• export_graphs_all(session_id="default"): Export all in-memory graphs. Returns file paths.

• get_help(topic, plain_text=False, session_id="default"): Markdown-rendered Stata help.

• codebook(variable, as_json=True, trace=False, raw=False, max_output_lines=None, session_id="default"): Variable-level metadata.

• run_do_file(path, echo=True, as_json=True, trace=False, raw=False, max_output_lines=None, session_id="default"): Execute a .do file in the specified session.

• get_stored_results(session_id="default"): Get r() and e() scalars/macros as JSON.

• get_variable_list(session_id="default"): JSON list of variables and labels.

• create_session(session_id): Manually create a new Stata session.

• list_sessions(): List all active sessions and their status.

• stop_session(session_id): Terminate a specific session.

• break_session(session_id="default"): Interrupt/Break the currently running command in a specific session. Use this if a command is taking too long and you want to stop it without closing the session and losing your data.

Cancellation

• Clients may cancel an in-flight request by sending the MCP notification notifications/cancelled with params.requestId set to the original tool call ID.

• Client guidance:

  1. Pass a _meta.progressToken when invoking the tool if you want progress updates (optional).

  2. If you need to cancel, send notifications/cancelled with the same requestId. You may also stop tailing the log file path once you receive cancellation confirmation (the tool call will return an error indicating cancellation).

  3. Be prepared for partial output in the log file; cancellation is best-effort and depends on Stata surfacing BreakError.

Resources exposed for MCP clients:

• stata://data/summary → summarize

• stata://data/metadata → describe

• stata://graphs/list → graph list (resource handler delegates to list_graphs tool)

• stata://variables/list → variable list (resource wrapper)

• stata://results/stored → stored r()/e() results

UI-only Data Browser (Local HTTP API)

This server also hosts a localhost-only HTTP API intended for a VS Code extension UI to browse data at high volume (paging, filtering) without sending large payloads over MCP.

Important properties:

• Loopback only: binds to 127.0.0.1.

• Bearer auth: every request requires an Authorization: Bearer <token> header.

• Short-lived tokens: clients should call get_ui_channel() to obtain a fresh token as needed.

• Session Isolate: caches (views, sorting) are isolated per sessionId.

• No Stata dataset mutation for browsing/filtering:

  • No generated variables.

  • Paging uses sfi.Data.get.

  • Filtering is evaluated in Python over chunked reads.

Discovery via MCP (get_ui_channel)

Call the MCP tool get_ui_channel() and parse the JSON:

Server-enforced limits (current defaults):

• maxLimit: 500

• maxVars: 32,767

• maxChars: 500

• maxRequestBytes: 1,000,000

• maxArrowLimit: 1,000,000 (specific to /v1/arrow)

Endpoints

All endpoints are under baseUrl and require the bearer token.

• GET /v1/dataset?sessionId=default

  • Returns dataset identity and basic state (id, frame, n, k) for the given session.

• GET /v1/vars?sessionId=default

  • Returns full variable list with labels, types, and formats.

• POST /v1/page

  • Paged data retrieval. Supports sortBy, filterExpr (ephemeral), and sessionId.

• POST /v1/arrow

  • Returns a binary Arrow IPC stream (same input as /v1/page).

• POST /v1/views

  • Create a long-lived filtered view. Returns a viewId. Requires sessionId.

• POST /v1/views/<viewId>/page

  • Paged retrieval from a previously created view. Supports sortBy and sessionId.

• POST /v1/views/:viewId/arrow

  • Returns a binary Arrow IPC stream from a filtered view.

• DELETE /v1/views/:viewId

  • Deletes a view handle.

• POST /v1/filters/validate

  • Validates a filter expression.

Paging request example

Sorting

The /v1/page and /v1/views/:viewId/page endpoints support sorting via the optional sortBy parameter:

Sort specification format:

• sortBy is an array of strings (variable names with optional prefix)

• No prefix or + prefix = ascending order (e.g., "price" or "+price")

• - prefix = descending order (e.g., "-price")

• Multiple variables are supported for multi-level sorting

• Uses the native Rust sorter when available, with a Polars fallback

Sorting with filtered views:

• Sorting is fully supported with filtered views

• The sort is computed in-memory over the sort columns, then filtered indices are re-applied

• Example: Filter for price < 5000, then sort descending by price

Notes:

• datasetId is used for cache invalidation. If the dataset changes due to running Stata commands, the server will report a new dataset id and view handles become invalid.

• Filter expressions are evaluated in Python using values read from Stata via sfi.Data.get. Use boolean operators like ==, !=, <, >, and and/or (Stata-style &/| are also accepted).

• Sorting does not mutate the dataset order in Stata; it computes sorted indices for the response and caches them for subsequent requests.

• The Rust sorter is the primary implementation; Polars is used only as a fallback when the native extension is unavailable.

License

This project is licensed under the GNU Affero General Public License v3.0 or later. See the LICENSE file for the full text.

Error reporting

• All tools that execute Stata commands support JSON envelopes (as_json=true) carrying:

  • rc (from r()/c(rc)), stdout, stderr, message, optional line (when Stata reports it), command, optional log_path (for log-file streaming), and a snippet excerpt of error output.

• Stata-specific cues are preserved:

  • r(XXX) codes are parsed when present in output.

  • “Red text” is captured via stderr where available.

  • trace=true adds set trace on around the command/do-file to surface program-defined errors; the trace is turned off afterward.

Logging

Set MCP_STATA_LOGLEVEL (e.g., DEBUG, INFO) to control server logging. Logs include discovery details (edition/path) and command-init traces for easier troubleshooting.

Development & Contributing

For detailed information on building, testing, and contributing to this project, see CONTRIBUTING.md.

Quick setup: