Dr. QuantMaster MCP Server

Dr. QuantMaster MCP Server

AI-powered quantitative research assistant with 45 tools for causal inference methods (DID, RDD, IV, PSM), regression analysis, power calculations, and statistical code generation in R, Stata, and Python.

Category
访问服务器

README

Dr. QuantMaster MCP Server

AI-Powered Quantitative Research Assistant with 45 MCP tools for causal inference, regression analysis, power calculation, and statistical code generation.

Features

45 MCP Tools in 10 Categories

Category Tools Description
Knowledge Search 5 Search statistical knowledge, method guides, formula lookup
Sample Size & Power 5 Power analysis, effect size, MDE calculator
Diagnostics 5 Assumption checks, regression diagnostics, test selection
Causal Inference 6 DID, RDD, IV, PSM, Synthetic Control guides
Code Generation 8 R, Stata, Python code generation and optimization
Interpretation 5 Coefficient interpretation, model fit, results writing
Meta-Analysis 4 Effect sizes, heterogeneity, publication bias
Reporting 5 Journal guidelines, APA reporting, preregistration
Advanced Methods 5 SEM, MLM, Bayesian, ML for causal, time series
File Operations 2 Analysis file writing, project structure creation

Causal Inference Methods Supported

  • DID (Difference-in-Differences): Parallel trends, staggered adoption, event studies
  • RDD (Regression Discontinuity): Sharp/Fuzzy RDD, bandwidth selection, McCrary test
  • IV (Instrumental Variables): 2SLS, weak instrument tests, overidentification
  • PSM (Propensity Score Matching): Balance diagnostics, caliper selection, ATT/ATE
  • Synthetic Control: Donor pool selection, placebo tests, inference

Code Generation

Generate analysis code for:

  • R: tidyverse, fixest, did, rdrobust, MatchIt
  • Stata: reghdfe, did_imputation, rdrobust, psmatch2
  • Python: statsmodels, linearmodels, causalinference

Architecture

Skills (Hot Layer)     MCP Tools (Cold Layer)     RAG (Vector Search)
      |                        |                         |
      v                        v                         v
 01_IDENTITY.md          45 Tools               32 ChromaDB Collections
 02_CAUSAL_INFERENCE.md  - Knowledge Search     - stat_foundations
 03_REGRESSION.md        - Power Analysis       - regression_*
                         - Code Generation      - econometrics_*
                         - Diagnostics          - advanced_*

Installation

Prerequisites

  • Node.js 18+
  • npm or yarn

Setup

# Clone the repository
git clone https://github.com/seanshin0214/quantmaster-mcp-server.git
cd quantmaster-mcp-server

# Install dependencies
npm install

# Build
npm run build

# Copy environment file
cp .env.example .env

Claude Desktop Configuration

Add to claude_desktop_config.json:

Windows:

{
  "mcpServers": {
    "quantmaster": {
      "command": "node",
      "args": ["C:\\path\\to\\quantmaster-mcp-server\\dist\\index.js"],
      "env": {
        "CHROMA_PATH": "C:\\path\\to\\quantmaster-mcp-server\\chroma-data"
      }
    }
  }
}

macOS/Linux:

{
  "mcpServers": {
    "quantmaster": {
      "command": "node",
      "args": ["/path/to/quantmaster-mcp-server/dist/index.js"],
      "env": {
        "CHROMA_PATH": "/path/to/quantmaster-mcp-server/chroma-data"
      }
    }
  }
}

Usage Examples

Power Analysis

Tool: calc_power
Input: { "n": 200, "effectSize": 0.3, "alpha": 0.05 }

Causal Inference Guide

Tool: causal_design_guide
Input: { "method": "did", "context": "policy evaluation" }

Generate R Code

Tool: generate_r_code
Input: {
  "method": "did",
  "dataDescription": "panel data with treatment in 2020"
}

Interpret Coefficient

Tool: interpret_coefficient
Input: {
  "coefficient": 0.15,
  "se": 0.05,
  "method": "ols",
  "outcomeVar": "log_wage"
}

Tool Reference

Knowledge Search Tools

  • search_stats_knowledge: Search statistical methods database
  • get_method_guide: Get detailed method guide
  • suggest_method: Suggest appropriate method for research question
  • compare_methods: Compare two statistical methods
  • get_formula: Get formula for specific statistic

Power Analysis Tools

  • calc_sample_size: Calculate required sample size
  • calc_power: Calculate statistical power
  • calc_effect_size: Calculate effect size from statistics
  • mde_calculator: Calculate minimum detectable effect
  • power_curve: Generate power curve data

Causal Inference Tools

  • causal_design_guide: Get causal inference design guide
  • parallel_trends_check: Check parallel trends assumption
  • iv_strength_check: Check instrument strength
  • psm_guide: Propensity score matching guide
  • rdd_bandwidth: RDD bandwidth selection guide
  • event_study_guide: Event study design guide

Code Generation Tools

  • generate_r_code: Generate R analysis code
  • generate_stata_code: Generate Stata analysis code
  • generate_python_code: Generate Python analysis code
  • code_template: Get code template for method
  • visualization_code: Generate visualization code
  • table_code: Generate publication-ready table code
  • debug_code: Debug statistical code
  • optimize_code: Optimize code performance

32 ChromaDB Collections

Domain Collections
Foundations stat_foundations, probability_theory, inference_basics
Regression regression_ols, regression_diagnostics, regression_extensions
Econometrics econometrics_panel, econometrics_iv, econometrics_did, econometrics_rdd
Advanced advanced_sem, advanced_mlm, advanced_bayesian, advanced_ml_causal
Meta-Analysis meta_effect_sizes, meta_heterogeneity, meta_publication_bias
Code code_r, code_stata, code_python

Skills Files

01_IDENTITY.md

Dr. QuantMaster persona and core capabilities definition.

02_CAUSAL_INFERENCE.md

Detailed guides for DID, RDD, IV, PSM, and Synthetic Control with code templates.

03_REGRESSION.md

OLS, Panel Data, Limited Dependent Variables, Count Models, and Survival Analysis guides.

License

MIT License - See LICENSE for details.

Author

Sean Shin (@seanshin0214)

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.


Built with Model Context Protocol and ChromaDB

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选