aigroup-econ-mcp 2.0.10

Econometrics MCP server with regression, causal inference, time series, panel data, and statistical analysis workflows.

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, 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

macOS users: install libomp for XGBoost-backed tools

A subset of machine-learning tools links against xgboost, which requires the OpenMP runtime (libomp.dylib) at import time. Without it, these four tools return a tool_unavailable payload even though the server itself starts fine:

• ml_kmeans_clustering
• ml_hierarchical_clustering
• ml_double_machine_learning
• ml_causal_forest

Install once with Homebrew:

brew install libomp

Linux / Windows users are unaffected — the xgboost wheels bundle or locate OpenMP automatically.

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 (default — structured Pydantic result serialized)
• markdown (human-readable tables and coefficient stars)
• text (compact str(model.model_dump()) fallback)

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

Calling tools

Every tool accepts parameters either inline (direct lists) or from a file via file_path. The server returns a JSON string; on failure the payload has a uniform {"ok": false, "error": {...}} shape.

Inline: OLS regression

{
  "name": "basic_parametric_estimation_ols",
  "arguments": {
    "y_data": [1.0, 2.1, 2.9, 4.1, 5.0, 5.9, 7.1, 8.0, 8.9, 10.1],
    "x_data": [[1.0], [2.0], [3.0], [4.0], [5.0],
               [6.0], [7.0], [8.0], [9.0], [10.0]],
    "output_format": "json"
  }
}

File-based: causal DID from a CSV

Your CSV first column is the dependent variable (y_data), the rest are covariates (x_data). For domain-specific keys like treatment, time_period, outcome, supply a .json file:

// policy.json
{"treatment": [0,0,1,1,0,0,1,1],
 "time_period": [0,1,0,1,0,1,0,1],
 "outcome":    [4.1,4.8,3.9,5.6,4.0,4.7,3.8,5.5]}

{
  "name": "causal_difference_in_differences",
  "arguments": {"file_path": "policy.json", "output_format": "markdown"}
}

Time series: ARIMA with forecast

{
  "name": "time_series_arima_model",
  "arguments": {
    "data": [/* monthly observations */],
    "order": [1, 1, 1],
    "forecast_steps": 6,
    "output_format": "markdown"
  }
}

Interpreting fit_warnings

Several models return a fit_warnings array. A non-empty value means the statistic was a fallback placeholder — treat the associated numbers with care. For example, a Cox regression with singular Hessian:

{
  "coefficients": [...],
  "std_errors": [1.0, 1.0, 1.0],
  "p_values":   [1.0, 1.0, 1.0],
  "fit_warnings": [
    "Hessian inversion failed; std_errors are placeholder 1.0 — Z/p values below are not real"
  ]
}

Seeing p=1.0 with no warning means "not significant"; the same with a warning means "could not compute".

Debug mode

Set AIGROUP_ECON_MCP_DEBUG=1 before launching the server to include Python tracebacks inside the structured error payload — useful when developing new MCP clients.

Project Structure

aigroup-econ-mcp/
├── aigroup_econ_mcp/       # MCP server + CLI
│   ├── cli.py              #   argparse entry point
│   ├── server.py           #   FastMCP wire-up
│   ├── registry.py         #   ToolSpec registry
│   ├── _registrations.py   #   all 66 tools registered here
│   └── errors.py
├── tools/                  # adapter layer (I/O + formatting)
├── econometrics/           # algorithms
├── resources/
├── prompts/
├── docs/
│   ├── ARCHITECTURE.md
│   ├── PUBLISHING.md
│   └── TESTING.md
├── tests/
├── CHANGELOG.md
└── pyproject.toml

See docs/ARCHITECTURE.md for layer boundaries and how to add a new tool.

Development

uv sync
uv run pytest           # full suite, ~12 s (271 tests)
uv run pytest -m "not slow"   # fast iteration, ~1.5 s (264 tests)
uv run ruff check .
uv run ruff format .

All 66 registered tools are covered by a four-tier test pyramid: registration-shape → smoke → mathematical-correctness (known-DGP) → real MCP stdio protocol. See docs/VERIFICATION.md for the testing paradigm, ground-truth DGPs, tolerance rationale, and the record of real bugs this approach has surfaced (9 to date). See docs/TESTING.md for quick-run commands and the test-file layout.

Troubleshooting

uvx resolves an old version

uvx caches per-version, so if a published release is not picked up:

uvx --refresh aigroup-econ-mcp
# or
uv cache clean

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