Looking at write_note, I can see it's a complete content replacement tool. An edit_note() tool would be really valuable for incremental changes. Here's my thinking:

  Use Cases for edit_note():

  - Append new sections to existing notes (most common)
  - Update specific information without rewriting everything
  - Add observations/relations to existing content
  - Fix typos or update facts
  - Prepend updates like meeting notes with timestamps

  Proposed Design:

  @mcp.tool()
  async def edit_note(
      identifier: str,        # title, permalink, or memory:// URL  
      operation: str,         # "append", "prepend", "replace_section", "find_replace"
      content: str,           # content to add/replace
      section: str = None,    # for replace_section - header name like "## Notes"
      find_text: str = None,  # for find_replace
  ) -> str:

  Operations:

  1. append - Add content to end (most useful)
  edit_note("project-planning", "append", "\n## New Requirements\n- Feature X\n- Feature Y")
  2. prepend - Add content to beginning
  edit_note("meeting-notes", "prepend", "## 2025-05-25 Update\n- Progress on feature...")
  3. replace_section - Replace content under specific header
  edit_note("specs/api", "replace_section", "New API design...", section="## Implementation")
  4. find_replace - Simple text replacement
  edit_note("config", "find_replace", "v0.13.0", find_text="v0.12.0")

  Implementation Flow:

  1. Use read_note() internally to get current content
  2. Apply the specified operation
  3. Use existing PUT /knowledge/entities/{permalink} endpoint
  4. Return similar summary as write_note()

  This would be perfect for AI assistants making targeted updates without having to regenerate entire documents. The append operation alone would handle 80% of use cases.

  Want me to implement this? I'd start with just append and prepend for v0.13.0 to keep it simple.