mcp-stata 3.0.3

A lightweight Model Context Protocol (MCP) server for Stata. Execute commands, inspect data, retrieve stored results (`r()`/`e()`), and view graphs in your chat interface. Built for economists who want to integrate LLM assistance into their Stata workflow.

Stata MCP Server (mcp-stata)

 

mcp-stata is an agentic toolkit for empirical researchers. It gives AI agents native control over a local Stata installation, allowing agents to run do-files, inspect data, check stored results, and export graphs. Contains a skills catalog for workflows researchers use often: auditing data, replication and robustness checks, specification comparisons, publication QA, referee responses, and modernization of legacy code. Featured in News.

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: Stata Workbench.

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.
• Drive paper workflows: run structured research audits, estimation planning, specification comparisons, publication checks, and reproducibility diagnostics.
• Use modern MCP surfaces: discover prompts, project/session resources, artifacts, and safety metadata through structured tool envelopes.

Quickstart

1 · Install

macOS/Linux:

curl -LsSf https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.sh | bash

Windows (PowerShell):

irm https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.ps1 | iex

Client-specific examples:

Client	macOS/Linux	Windows (PowerShell)
Claude Code	bash <(curl -LsSf https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.sh) --agent claude	& ([scriptblock]::Create((irm https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.ps1))) --agent claude
Codex	bash <(curl -LsSf https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.sh) --agent codex	& ([scriptblock]::Create((irm https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.ps1))) --agent codex
Gemini	bash <(curl -LsSf https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.sh) --agent gemini	& ([scriptblock]::Create((irm https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.ps1))) --agent gemini
Cursor	bash <(curl -LsSf https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.sh) --agent cursor	& ([scriptblock]::Create((irm https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.ps1))) --agent cursor
Windsurf	bash <(curl -LsSf https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.sh) --agent windsurf	& ([scriptblock]::Create((irm https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.ps1))) --agent windsurf
VS Code	bash <(curl -LsSf https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.sh) --agent vscode	& ([scriptblock]::Create((irm https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.ps1))) --agent vscode
Auto-detect / default	bash <(curl -LsSf https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.sh)	& ([scriptblock]::Create((irm https://raw.githubusercontent.com/tmonk/mcp-stata/main/plugin/install.ps1)))

2 · Verify

Ask your agent:

Do you have access to the Stata agentic toolkit? (mcp-stata)

It will confirm the connection and describe all available tools and skills.

3 · Try it

Load the auto dataset and run a regression of price on mpg

Academic Research Workflows

The toolkit is designed for empirical economics research.

• Replication and robustness: rerun pipelines, compare specifications, and preserve an audit trail.
• Data audit: check structure, missingness, duplicate identifiers, suspicious coding, and documentation readiness.
• Publication QA: review tables and figures for paper-ready presentation.
• Referee response: organize reruns and evidence for critiques or coauthor requests.
• Environment diagnosis: troubleshoot Stata discovery, package availability, graph export, and managed-machine quirks.
• Safety and diagnostics: diagnose the MCP server, and enforce the safety of code run through the server.

Prerequisites

• Stata 17+ (Stata MP, SE, or BE). Must be licensed and installed locally.
• Python 3.11+
• uv (recommended)

Note on pystata: 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.

Run as a published tool with uvx

uvx --refresh --refresh-package mcp-stata --from mcp-stata@latest mcp-stata

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:

# macOS example
export STATA_PATH="/Applications/StataNow/StataMP.app/Contents/MacOS/stata-mp"

# Windows example (cmd.exe)
set STATA_PATH="C:\Program Files\Stata18\StataMP-64.exe"

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

# Example
export MCP_STATA_TEMP="/path/to/writable/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/)

Startup Do Files

When a session starts, MCP-Stata loads startup do files in the same order as native Stata:

1. MCP_STATA_STARTUP_DO_FILE (env var) — one or more custom do files, separated by : (Unix) or ; (Windows).
2. sysprofile.do — the first one found along the Stata search path.
3. profile.do — the first one found along the Stata search path.

The search path mirrors native Stata: Stata install directory, current working directory, then the ado-path (PERSONAL, SITE, PLUS, OLDPLACE, ...). Only the first sysprofile.do and first profile.do found are executed, matching native Stata behavior. All paths are deduplicated so the same file is never run twice.

If a command clears programs (clear all, clear programs, or program drop _all), MCP-Stata automatically re-executes the startup files so that any programs they defined remain available. To disable this and let clear all behave exactly as in native Stata (programs are lost), set:

MCP_STATA_NO_RELOAD_ON_CLEAR=1

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):

"env": {
  "STATA_PATH": "/Applications/StataNow/StataMP.app/Contents/MacOS/stata-mp",
  "MCP_STATA_STARTUP_DO_FILE": "/path/to/my/startup.do",
  "MCP_STATA_NO_RELOAD_ON_CLEAR": "1"
}

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)

{
  "mcpServers": {
    "mcp-stata": {
      "command": "uvx",
        "args": [
        "--refresh",
        "--refresh-package",
        "mcp-stata",
        "--from",
        "mcp-stata@latest",
        "mcp-stata"
      ]
    }
  }
}

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)

{
  "mcpServers": {
    "mcp-stata": {
      "command": "uvx",
       "args": [
        "--refresh",
        "--refresh-package",
        "mcp-stata",
        "--from",
        "mcp-stata@latest",
        "mcp-stata"
      ]
    }
  }
}

---

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)

{
  "mcpServers": {
    "mcp-stata": {
      "command": "uvx",
        "args": [
        "--refresh",
        "--refresh-package",
        "mcp-stata",
        "--from",
        "mcp-stata@latest",
        "mcp-stata"
      ]
    }
  }
}

---

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)

{
  "mcpServers": {
    "mcp-stata": {
      "command": "uvx",
        "args": [
        "--refresh",
        "--refresh-package",
        "mcp-stata",
        "--from",
        "mcp-stata@latest",
        "mcp-stata"
      ]
    }
  }
}

---

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)

{
  "servers": {
    "mcp-stata": {
      "type": "stdio",
      "command": "uvx",
      "args": [
        "--refresh",
        "--refresh-package",
        "mcp-stata",
        "--from",
        "mcp-stata@latest",
        "mcp-stata"
      ]
    }
  }
}

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

---

Skills Catalog

The toolkit includes a catalog of "Skills", providing domain knowledge to AI agents.

• Base Skill: skill/SKILL.md — Main Stata toolkit dispatcher.
• Modernize Skill: modernize/SKILL.md — Replaces legacy Stata patterns (i.e. prefer frames over preserve, restore.)
• Replication Skill: replication/SKILL.md — Reproducibility and robustness workflows.
• Data Audit Skill: data-audit/SKILL.md — Dataset QA and sanity checks.
• Publication QA Skill: publication-qa/SKILL.md — Tables and figures for paper readiness.
• Environment Diagnose Skill: environment-diagnose/SKILL.md — Setup and platform troubleshooting.
• Additional plugin skills cover basic causal inference, table building, power analysis, data provenance, and referee-response work.

Tools Available (from server.py)

• stata_run(code, is_file=False, background=False, echo=True, as_json=True, trace=False, raw=False, max_output_lines=None, cwd=None, session_id="default", strip_smcl=True, filter_pattern=None, exclude_pattern=None): Execute Stata commands or a .do file.
  • Set is_file=True to treat code as an absolute path to a .do file.
  • Set background=True to start long jobs asynchronously (returns task_id).
  • Always writes output to a temporary log file and emits notifications/logMessage containing {"event":"log_path","path":"..."}.
  • May emit notifications/progress when the client provides a progress token/callback.
• stata_task_status(task_id, wait=False, timeout=60.0, poll_interval=1.0, tail_lines=0): Query or wait on background task status.
• stata_control(action, id): Control active work.
  • action="break" with id=<session_id> to interrupt a running session.
  • action="cancel" with id=<task_id> to cancel a background task.
• stata_read_log(path, offset=0, max_bytes=262144, tail_lines=0, query=None, before=2, after=2, case_sensitive=False, regex=False, max_matches=50): Read, tail, or search a log file.
• stata_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.
• stata_inspect_data(action, query=None, variables=None, start=0, count=50, session_id="default"): Unified data inspector.
  • action: describe, codebook, summary, search, list, get, or lint.
  • lint: performs static analysis of .do and .ado files for modern best practices.
• stata_manage_graphs(action, graph_name=None, format="svg", session_id="default"): Graph management (list, export, export_all).
• stata_get_results(session_id="default", include_formatting=False, include_matrices=True, matrix_max_rows=200, matrix_max_cols=200, include_mata=False, as_json=True): Unified stored-results tool for coherent structured r()/e()/s() payloads with optional structured Mata snapshot.
• stata_get_help(topic, plain_text=False, merge_paragraphs=True, session_id="default"): Markdown or plain-text Stata help.
• stata_manage_session(action, session_id="default", code=None, since_command=None): Session lifecycle, state history, and UI channel orchestration.
  • action: create, stop, list, set_profile, history_diff, history_stats, get_ui_channel, or detect.
  • detect: Returns metadata about the Stata installation (version, flavor, OS) and optionally SSC packages.
  • history_diff returns tracked changes in variables/macros and dataset dimensions.
  • history_stats returns retained window metadata (history_size, earliest_command, latest_command, max_history_entries).

Common action examples

# Session lifecycle
stata_manage_session(action="create", session_id="analysis")
stata_manage_session(action="list")
stata_manage_session(action="stop", session_id="analysis")

# Session history tracking
stata_manage_session(action="history_stats", session_id="analysis")
stata_manage_session(action="history_diff", session_id="analysis")
stata_manage_session(action="history_diff", session_id="analysis", since_command=42)

# Run a do-file (replacement for run_do_file)
stata_run("/path/to/analysis.do", is_file=True, session_id="analysis")

# Data inspection (describe, codebook, variable list)
stata_inspect_data(action="describe", session_id="analysis")
stata_inspect_data(action="codebook", query="price", session_id="analysis")
stata_inspect_data(action="list", session_id="analysis")

# Graph operations
stata_manage_graphs(action="list", session_id="analysis")
stata_manage_graphs(action="export", graph_name="Graph", format="png", session_id="analysis")
stata_manage_graphs(action="export_all", session_id="analysis")

# Help and stored results
stata_get_help(topic="regress", session_id="analysis")
stata_get_results(session_id="analysis", include_mata=True)

# UI data browser channel
stata_manage_session(action="get_ui_channel", session_id="analysis")

# Interrupt / cancel / background status
stata_control(action="break", id="analysis")
stata_run("quietly do /path/to/long_job.do", background=True, session_id="analysis")
stata_task_status(task_id="...", wait=True, timeout=30)
stata_control(action="cancel", id="...")

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.

Output and results behavior

• stata_run defaults to strip_smcl=True, so responses are plain-text oriented unless you explicitly disable stripping.
• stata_get_results returns structured stored results and can include Mata state (include_mata=True) with typed object/function payloads suitable for downstream programmatic checks.

Resources exposed for MCP clients:

• stata://data/summary → summarize
• stata://data/metadata → describe
• stata://graphs/list → graph list (resource handler delegates to stata_manage_graphs(action="list"))
• stata://variables/list → variable list (resource wrapper)
• stata://results/stored → stored r()/e()/s() 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 stata_manage_session(action="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 (stata_manage_session)

Call the MCP tool stata_manage_session(action="get_ui_channel") and parse the JSON:

{
  "baseUrl": "http://127.0.0.1:53741",
  "token": "...",
  "expiresAt": 1730000000,
  "capabilities": {
    "dataBrowser": true,
    "filtering": true,
    "sorting": true,
    "arrowStream": true
  }
}

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

curl -sS \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"datasetId":"...","frame":"default","offset":0,"limit":50,"vars":["price","mpg"],"includeObsNo":true,"maxChars":200}' \
  "$BASE_URL/v1/page"

Sorting

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

# Sort by price ascending
curl -sS \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"datasetId":"...","offset":0,"limit":50,"vars":["price","mpg"],"sortBy":["price"]}' \
  "$BASE_URL/v1/page"

# Sort by price descending
curl -sS \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"datasetId":"...","offset":0,"limit":50,"vars":["price","mpg"],"sortBy":["-price"]}' \
  "$BASE_URL/v1/page"

# Multi-variable sort: foreign ascending, then price descending
curl -sS \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"datasetId":"...","offset":0,"limit":50,"vars":["foreign","price","mpg"],"sortBy":["foreign","-price"]}' \
  "$BASE_URL/v1/page"

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

# Create a filtered view
curl -sS \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"datasetId":"...","frame":"default","filterExpr":"price < 5000"}' \
  "$BASE_URL/v1/views"
# Returns: {"view": {"id": "view_abc123", "filteredN": 37}}

# Get sorted page from filtered view
curl -sS \
  -H "Authorization: Bearer $TOKEN" \
  -H "Content-Type: application/json" \
  -d '{"offset":0,"limit":50,"vars":["price","mpg"],"sortBy":["-price"]}' \
  "$BASE_URL/v1/views/view_abc123/page"

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:

# Install dependencies
uv sync --extra dev --no-install-project

# Run tests (requires Stata)
pytest

# Run tests without Stata
pytest -v -m "not requires_stata"

# Build the package
python -m build