Overview

RMCP (Research Model Context Protocol) is a purpose‑built MCP server that turns natural language queries into full‑blown statistical analyses. By exposing a rich set of 44 tools across eleven domains—regression, econometrics, time‑series forecasting, machine learning, statistical testing, data transformation, visualization, file I/O, and NLP‑friendly helpers—RMCP lets developers embed sophisticated data science workflows directly into AI assistants like Claude. Instead of manually writing code or calling separate APIs, a user can simply say, “Show me the correlation between marketing spend and sales,” and RMCP will import the data, fit a model, generate diagnostics, and return an inline plot—all through the assistant’s chat interface.

The server is especially valuable for teams that need rapid prototyping or exploratory analysis without a full data‑science stack. Developers can wire RMCP into existing MCP workflows, enabling AI agents to act as on‑demand analysts. Because the tools are wrapped in MCP resources, they can be combined with other services—such as database connectors or custom model servers—to create end‑to‑end pipelines that start with a natural language prompt and finish with actionable insights.

Key capabilities include:

• Econometric modeling: Linear, logistic, panel data, and instrumental‑variable regressions with automatic inference and diagnostics.
• Time‑series analysis: ARIMA, decomposition, stationarity tests, and forecasting tools that return both forecasts and confidence intervals.
• Machine learning primitives: Clustering, decision trees, random forests, with built‑in cross‑validation and feature importance reporting.
• Statistical testing: T‑tests, ANOVA, chi‑square, normality checks—all accessible via conversational commands.
• Data preparation: Standardization, winsorization, lag/lead creation, and outlier handling to ready data for modeling.
• Visualization: Inline plots (scatter, histograms, heatmaps) rendered directly in the assistant’s UI, so users can see results without leaving the chat.
• File handling: Robust CSV/Excel/JSON import with schema validation, making it easy to ingest real datasets.
• Natural‑language helpers: Formula construction suggestions, error recovery prompts, and example datasets that guide users through complex analyses.

Typical use cases span business analytics (ROI calculations, churn prediction), economic research (testing Okun’s law, GDP‑unemployment relationships), and customer segmentation. By integrating RMCP into AI workflows, developers can offer non‑technical stakeholders a conversational interface to deep statistical insight, accelerating decision making and reducing the barrier to entry for data‑driven projects.