aigroup-econ-mcp

Econometrics MCP server for regression, causal inference, time series, panel data, machine learning, and broader statistical analysis workflows.

Overview

aigroup-econ-mcp is a professional econometrics-oriented MCP server designed to help AI assistants and MCP clients perform structured quantitative analysis.

It covers:

• parameter estimation and regression analysis
• causal inference workflows
• microeconometrics and panel data
• time series and volatility models
• machine learning for econometric tasks
• spatial econometrics, decomposition, and inference tools

Highlights

• 66 professional tools across core econometrics domains
• Multiple input formats including CSV, JSON, TXT, and Excel
• Multiple output formats including JSON, Markdown, HTML, LaTeX, and text
• Support for MCP clients such as RooCode, Claude-compatible tools, and other MCP hosts
• Broad method coverage from OLS and IV to ARIMA, GARCH, GAM, and causal forests
• Designed for research and applied analysis rather than narrow single-task workflows

Tool Groups

The server currently groups its 66 tools across the following categories:

• Basic parametric estimation — OLS, MLE, GMM
• Causal inference — DID, IV, PSM, fixed/random effects, RDD, synthetic control, event study, and more
• Decomposition analysis — Oaxaca-Blinder, ANOVA, time-series decomposition
• Machine learning — random forest, gradient boosting, SVM, neural networks, clustering, DML, causal forest
• Microeconometrics — logit, probit, multinomial logit, Poisson, negative binomial, Tobit, Heckman
• Missing data handling — simple imputation and MICE
• Model diagnostics and robust inference — specification tests, GLS, WLS, robust errors, regularization, simultaneous equations
• Nonparametric methods — kernel regression, quantile regression, spline regression, GAM
• Spatial econometrics — weights matrices, Moran's I, Geary's C, LISA, spatial regression, GWR
• Statistical inference — bootstrap and permutation tests
• Time series and panel data — ARIMA, exponential smoothing, GARCH, unit-root tests, VAR/SVAR, cointegration, dynamic panel, panel VAR, structural breaks, time-varying parameter models

Quick Start

Requirements

• Python >= 3.10
• uvx recommended for easiest usage, or pip

Run with uvx

uvx aigroup-econ-mcp

If uvx keeps using an older cached build:

uvx --no-cache aigroup-econ-mcp

Install with pip

pip install aigroup-econ-mcp
aigroup-econ-mcp

MCP Client Configuration

Claude-compatible MCP clients / RooCode / similar tools

{
  "mcpServers": {
    "aigroup-econ-mcp": {
      "command": "uvx",
      "args": ["aigroup-econ-mcp"]
    }
  }
}

Input & Output Support

Supported input formats

• CSV
• JSON
• TXT
• Excel (.xlsx, .xls)

Typical usage patterns:

• direct structured data input
• raw file content input
• local file path input

Supported output formats

• json
• markdown
• html
• latex
• text

Example Use Cases

• OLS and generalized regression modeling
• difference-in-differences and instrumental variable analysis
• matching and regression discontinuity workflows
• random forest / gradient boosting / causal forest analysis
• ARIMA, GARCH, VAR, and cointegration modeling
• panel diagnostics and dynamic panel estimation

Project Structure

aigroup-econ-mcp/
├── econometrics/
├── tools/
├── resources/
├── prompts/
├── cli.py
├── server.py
└── pyproject.toml

Development

uv sync
uv run pytest

Useful development commands:

uv run black .
uv run isort .

Troubleshooting

uvx cache issue

If a newer published version does not seem to load, try one of the following:

uvx --no-cache aigroup-econ-mcp
uv cache clean

The repository also includes helper scripts such as:

• clear_uvx_cache.bat
• clear_uvx_cache.sh
• clear_uvx_cache.py

License & Usage

This project is released under the MIT License.

You may use, copy, modify, merge, publish, distribute, sublicense, and sell copies of this software, including in academic, research, internal, and commercial environments, provided that the original copyright notice and license text are preserved.

Please keep in mind:

• the software is provided "AS IS", without warranty of any kind
• you must retain the relevant copyright and permission notice in copies or substantial portions of the software
• statistical results still depend on data quality, assumptions, and correct methodological choices by the user

See the full text in LICENSE.

Acknowledgments

Core Scientific Ecosystem

• statsmodels — statistical modeling foundations
• pandas — data manipulation and tabular workflows
• scikit-learn — machine learning components
• linearmodels — panel data and econometric modeling support
• arch — volatility and ARCH/GARCH modeling

Community & Protocol Ecosystem

• Model Context Protocol — MCP integration model
• The broader econometrics and open-source scientific computing community

Support

• Issues: https://github.com/jackdark425/aigroup-econ-mcp/issues
• Repository: https://github.com/jackdark425/aigroup-econ-mcp
• PyPI publishing guide: PYPI_PUBLISH_GUIDE.md