Address viktor's feedback

We were lucky to be reviewed by Viktor on his youtube channel DevOps Toolkit. Here is the video.

There is tons of great feedback to improve the agent. I asked Gemini to summarize and give us a roadmap to address the feedback.

Okay, here's a concise and direct roadmap in markdown format:

Roadmap: Enhancing kubectl-ai Based on Video Feedback

Overall Goal: Transform kubectl-ai into a more intelligent, interactive, and flexible Kubernetes assistant.

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1. Enhanced Conversational Intelligence & Interaction

• Proactive Resource Creation:
  • Action: Prompt users for resource specifics (namespace, image, ports) before generating manifests.
  • Why: Improve manifest accuracy and user guidance.
• Smarter Diagnostics:
  • Action: Prioritize identifying cluster problems (failed/pending pods, errors) in diagnostic responses.
  • Why: Deliver more relevant and actionable insights.
• Interactive Disambiguation:
  • Action: Offer users choices (e.g., numbered lists) when multiple interpretations or options exist.
  • Why: Increase accuracy and user control.

2. Advanced Context Management & Memory

• Persistent Session Instructions:
  • Action: Enable users to define lasting behavioral preferences for the AI (e.g., "always confirm apply," "save manifests to file X").
  • Why: Reduce repetitive instructions and tailor AI behavior.
• MCP Client Capability:
  • Action: Investigate kubectl-ai acting as a client to external MCP servers.
  • Why: Allow leveraging shared/persistent memory and standardizing context management.

3. Agent Capabilities & Flexibility

• Refine Tool Usage Explanation:
  • Action: Improve AI's explanation of why it chooses specific commands.
  • Why: Increase transparency and user trust.
• (Future) Pluggable LLM Support:
  • Action: Architect for potential integration with other LLMs beyond Gemini.
  • Why: Offer greater user choice and adaptability.

4. Leveraging Strengths

• Promote MCP Server Functionality:
  • Action: Document and showcase kubectl-ai's capability to act as an MCP server for other agents.
  • Why: Highlight and capitalize on a key differentiating feature.

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Key Focus: Address current interaction and memory limitations to make kubectl-ai a more powerful and intuitive tool for Kubernetes management.