Overview

MCP Analyst is a lightweight MCP server designed to bridge the gap between large tabular datasets and AI assistants such as Claude. When working with CSV or Parquet files that exceed the model’s context window, or when uploading entire datasets would be cost‑prohibitive, MCP Analyst allows the assistant to query and analyze data locally. This eliminates the need for cloud storage or expensive data‑transfer operations while keeping the full dataset on the user’s machine.

The server exposes a simple set of capabilities: it accepts file paths (including glob patterns) and returns structured insights, summaries, or results of arbitrary queries. By keeping the data on‑premises, developers preserve privacy and compliance with internal policies that forbid external uploads. Additionally, the local execution model reduces latency compared to remote API calls, enabling real‑time exploratory analysis during a conversation with the assistant.

Key features include:

• File discovery via globbing – specify patterns such as to load multiple files in a single request.
• Support for both CSV and Parquet formats – covers the most common tabular storage options in data science workflows.
• Context‑aware querying – the assistant can ask for aggregates, filters, or visual summaries without sending raw data to external services.
• Cost optimization – only the requested subset of the dataset is processed, keeping bandwidth and token usage minimal.

Typical use cases span data exploration, business intelligence, and rapid prototyping. A data analyst can ask Claude to compute monthly sales trends or identify outliers, while a developer can integrate the server into an automated pipeline that feeds insights back into a dashboard. In research settings, MCP Analyst allows investigators to keep sensitive patient records on local machines while still leveraging AI for hypothesis generation.

Integration into MCP workflows is straightforward: once the server is registered in Claude’s configuration, any prompt that references the server automatically routes the request to MCP Analyst. The assistant then performs the necessary file operations and returns results in a conversational format, seamlessly blending code‑free data analysis with natural language interaction. This tight coupling between local data and AI reasoning gives developers a powerful, privacy‑preserving tool for turning raw tables into actionable knowledge.