{"$schema":"https://wellknown.network/schemas/agent-record-v1.json","schemaVersion":"1","id":"ag_t73krgjx8duq","handle":"aigroup-econ-mcp","url":"https://wellknown.network/agents/aigroup-econ-mcp","links":{"self":"https://wellknown.network/agents/aigroup-econ-mcp/record.json","html":"https://wellknown.network/agents/aigroup-econ-mcp","markdown":"https://wellknown.network/agents/aigroup-econ-mcp/record.md","api":"https://wellknown.network/api/v1/agents/aigroup-econ-mcp","status":"https://wellknown.network/api/v1/agents/aigroup-econ-mcp/status","claim":"https://wellknown.network/agents/aigroup-econ-mcp/claim","claimApi":"https://wellknown.network/api/v1/claims","claimDescriptor":"https://wellknown.network/agents/aigroup-econ-mcp/claim.json","badge":"https://wellknown.network/agents/aigroup-econ-mcp/badge.svg","openapi":"https://wellknown.network/openapi.json","history":"https://wellknown.network/api/v1/agents/aigroup-econ-mcp/history","tools":"https://wellknown.network/api/v1/agents/aigroup-econ-mcp/tools"},"ard":{"identifier":"urn:air::server:aigroup-econ-mcp","type":"application/mcp-server-card+json"},"kind":"mcp_server","declared":{"name":"aigroup-econ-mcp","summary":"Econometrics MCP server with regression, causal inference, time series, panel data, and statistical analysis workflows.","description":"# aigroup-econ-mcp\n\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](./LICENSE)\n[![Python](https://img.shields.io/badge/Python-3.10+-blue.svg)](https://www.python.org/)\n[![Version](https://img.shields.io/badge/version-2.0.10-brightgreen.svg)](https://github.com/jackdark425/aigroup-econ-mcp)\n[![Tools](https://img.shields.io/badge/Tools-66-brightgreen.svg)](https://github.com/jackdark425/aigroup-econ-mcp)\n\n> Econometrics MCP server for regression, causal inference, time series, panel data, machine learning, and broader statistical analysis workflows.\n\n## Overview\n\n`aigroup-econ-mcp` is a professional econometrics-oriented MCP server designed to help AI assistants and MCP clients perform structured quantitative analysis.\n\nIt covers:\n\n- parameter estimation and regression analysis\n- causal inference workflows\n- microeconometrics and panel data\n- time series and volatility models\n- machine learning for econometric tasks\n- spatial econometrics, decomposition, and inference tools\n\n## Highlights\n\n- **66 professional tools** across core econometrics domains\n- **Multiple input formats** including CSV, JSON, TXT, and Excel\n- **Multiple output formats** including JSON, Markdown, and text\n- **Support for MCP clients** such as RooCode, Claude-compatible tools, and other MCP hosts\n- **Broad method coverage** from OLS and IV to ARIMA, GARCH, GAM, and causal forests\n- **Designed for research and applied analysis** rather than narrow single-task workflows\n\n## Tool Groups\n\nThe server currently groups its 66 tools across the following categories:\n\n- **Basic parametric estimation** — OLS, MLE, GMM\n- **Causal inference** — DID, IV, PSM, fixed/random effects, RDD, synthetic control, event study, and more\n- **Decomposition analysis** — Oaxaca-Blinder, ANOVA, time-series decomposition\n- **Machine learning** — random forest, gradient boosting, SVM, neural networks, clustering, DML, causal forest\n- **Microeconometrics** — logit, probit, multinomial logit, Poisson, negat…","publisher":null,"homepage":"https://github.com/jackdark425/aigroup-econ-mcp#readme","repository":"https://github.com/jackdark425/aigroup-econ-mcp#readme","version":"2.0.10","license":"MIT","protocols":["mcp"],"tags":["causal-inference","data-analysis","econometrics","mcp","model-context-protocol","panel-data","regression","statistics","time-series"],"pricing":null,"endpoints":[{"url":"pypi:aigroup-econ-mcp","type":"package_pypi","auth":null,"probeable":false}],"skills":null,"tools":null,"extra":null,"attribution":{"kind":"pypi","name":"pypi","license":"pypi","repoUrl":"pypi","summary":"pypi","version":"pypi","description":"pypi","homepageUrl":"pypi"}},"derived":{"capabilities":[{"slug":"documents.spreadsheets","name":"Spreadsheets","confidence":0.733,"provenance":"derived"}],"categories":["documents"],"language":"en"},"observed":{"status":"unknown","statusReason":"Distributed as a package to run locally; 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