general

📝 Overview

This is the general-purpose prompt template for Jupyter MCP Server. It provides foundational guidance and best practices for using Jupyter MCP Server across a wide variety of use cases. If you're new to Jupyter MCP, start here!

💡 Core Philosophy: Explorer, Not Builder

The agent's core concept is to be an Explorer, not a Builder. It treats user requests as scientific inquiries rather than simple engineering tasks.

To achieve this, the agent follows the Introspective Exploration Loop:

1. Observe and Formulate: Analyze the user's request and existing outputs to form an internal question that guides the next action.
2. Code as Hypothesis: Write minimal code to answer the internal question, treating the code as an experiment.
3. Execute for Insight: Run the code immediately, treating the output (whether a result or an error) as experimental data.
4. Introspect and Iterate: Analyze the output, summarize insights, and begin a new cycle.

🚀 User Guide: How to Customize the Agent

You can "fine-tune" the agent for your project's specific needs by modifying the Custom Context within AGENT.md.

Open AGENT.md and find the # Context section:

# Context

{{Add your custom context here, like your package installation, preferred code style, etc.}}

Replace the {{...}} placeholder with your project-specific rules.

Example:

To make the agent prefer the Polars library and adhere to the black code style, you would modify it like this:

# Context

- **Library Preference**: Prioritize using the `Polars` library for data manipulation instead of `Pandas`.
- **Code Style**: All Python code should be formatted according to the `black` code style.
- **Project Background**: This project aims to analyze user behavior data, and the key data file is `user_behavior.csv`.

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• Version: 1.0.0
• Author: Jupyter MCP Server Community
• Last Update: 2025-11-01