docs: add FAQ section for common questions

[meichuanyi]

Summary

Adds a comprehensive FAQ section to help users understand and use the n8n workflow vectorization and knowledge graph project.

Changes

• Added FAQ section covering:
  • General (what is n8n_agent, MCP integrations)
  • Setup & Installation (process workflows, configure env vars)
  • MCP Integrations (QDRANT, n8n, Workflow Validation)
  • Workflow Analysis (extracted info, validation categories)
  • Database Integration (QDRANT, Neo4j, Supabase)
  • Troubleshooting (connection issues, validation failures, help resources)

Benefits

• New users can quickly understand the project
• Clear instructions for each MCP integration
• Self-service troubleshooting for common issues

Testing

• FAQ content aligns with existing README
• Markdown formatting validated locally

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Pure documentation improvement, no code changes.

Summary by Sourcery

Add a comprehensive FAQ section to the README to answer common questions about using and integrating the project.

Documentation:

• Document general concepts of n8n_agent and MCP integrations in a new FAQ section.
• Add setup, environment configuration, and workflow processing guidance to the README FAQ.
• Describe MCP usage for QDRANT, n8n, and workflow validation through FAQ entries.
• Explain workflow analysis outputs and validation categories in the FAQ.
• Highlight database roles for QDRANT, Neo4j, and Supabase, plus troubleshooting tips for common connection and validation issues.

[sourcery-ai]

Reviewer's Guide

Adds a new FAQ section to the README to document common questions about n8n_agent, its MCP-based integrations, workflow analysis, database backends, and troubleshooting steps, without modifying any runtime code.

File-Level Changes

Change	Details	Files
Document a comprehensive FAQ section in the README covering usage, integrations, analysis features, and troubleshooting.	Append a new "Frequently Asked Questions (FAQ)" section after the roadmap in the README. Describe the overall purpose of n8n_agent and its use of Qdrant, Neo4j, and Supabase. Explain MCP integrations for n8n, Qdrant, Supabase, and workflow validation, including available MCP tools and commands. Provide setup and installation guidance such as running the workflow parser and configuring required environment variables. Detail what data is extracted during workflow analysis and which validation categories exist. Clarify the roles of each database integration (vector, graph, relational) and their current implementation status. Add troubleshooting guidance for common connectivity and validation issues, plus where to get additional help.	README.md

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[sourcery-ai]

Hey - I've left some high level feedback:

• Consider aligning terminology and capitalization throughout the FAQ (e.g., QDRANT vs Qdrant, n8n_agent vs n8n agent, MCP tool names) with the rest of the README and actual CLI/tool identifiers to avoid confusion.
• Some FAQ answers repeat content already described above (e.g., workflow analysis details, validation categories); you might instead link back to the relevant sections to keep the README shorter and avoid having to maintain the same information in two places.
• For items marked as "(Currently in development)" in the database integration section, clarify the current level of support or expected behavior so readers know whether these are safe to use or still experimental.

Prompt for AI Agents

Please address the comments from this code review:

## Overall Comments
- Consider aligning terminology and capitalization throughout the FAQ (e.g., `QDRANT` vs `Qdrant`, `n8n_agent` vs `n8n agent`, MCP tool names) with the rest of the README and actual CLI/tool identifiers to avoid confusion.
- Some FAQ answers repeat content already described above (e.g., workflow analysis details, validation categories); you might instead link back to the relevant sections to keep the README shorter and avoid having to maintain the same information in two places.
- For items marked as "(Currently in development)" in the database integration section, clarify the current level of support or expected behavior so readers know whether these are safe to use or still experimental.

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