⚡ Add aggressive content limiting to prevent MCP 25k token errors

- Implement smart content truncation at ~80k chars (~20k tokens)
- Preserve document structure when truncating (stop at natural breaks)
- Add clear truncation notices with guidance for smaller ranges
- Update chunking suggestions to use safer 8-page chunks
- Enhance recommendations to suggest 3-8 page ranges
- Prevent 29,869 > 25,000 token errors while maintaining usability

src/mcp_office_tools/server.py

@@ -375,10 +375,27 @@ async def convert_to_markdown(
             if "table_of_contents" in markdown_result:
                 result["table_of_contents"] = markdown_result["table_of_contents"]
         else:
-            # Include full content for smaller documents or page ranges
-            result["markdown"] = markdown_result["content"]
-            result["metadata"]["character_count"] = len(markdown_result["content"])
-            result["metadata"]["word_count"] = len(markdown_result["content"].split())
+            # Include content with automatic size limiting to prevent MCP errors
+            content = markdown_result["content"]
+            
+            # Apply aggressive content limiting to stay under 25k token limit
+            # Rough estimate: ~4 chars per token, leave buffer for metadata
+            max_content_chars = 80000  # ~20k tokens worth of content
+            
+            if len(content) > max_content_chars:
+                # Truncate but try to preserve structure
+                truncated_content = _smart_truncate_content(content, max_content_chars)
+                result["markdown"] = truncated_content
+                result["content_truncated"] = True
+                result["original_length"] = len(content)
+                result["truncated_length"] = len(truncated_content)
+                result["truncation_note"] = f"Content truncated to stay under MCP 25k token limit. Original: {len(content):,} chars, Shown: {len(truncated_content):,} chars. Use smaller page ranges for full content."
+            else:
+                result["markdown"] = content
+                result["content_truncated"] = False
+            
+            result["metadata"]["character_count"] = len(content)
+            result["metadata"]["word_count"] = len(content.split())
 
         # Add image info
         if include_images and markdown_result.get("images"):
@@ -1522,6 +1539,49 @@ def _extract_markdown_structure(content: str) -> dict[str, Any]:
     return structure
 
 
+def _smart_truncate_content(content: str, max_chars: int) -> str:
+    """Intelligently truncate content while preserving structure and readability."""
+    if len(content) <= max_chars:
+        return content
+    
+    lines = content.split('\n')
+    truncated_lines = []
+    current_length = 0
+    
+    # Try to preserve structure by stopping at a natural break point
+    for line in lines:
+        line_length = len(line) + 1  # +1 for newline
+        
+        # If adding this line would exceed limit
+        if current_length + line_length > max_chars:
+            # Try to find a good stopping point
+            if truncated_lines:
+                # Check if we're in the middle of a section
+                last_lines = '\n'.join(truncated_lines[-3:]) if len(truncated_lines) >= 3 else '\n'.join(truncated_lines)
+                
+                # If we stopped mid-paragraph, remove incomplete paragraph
+                if not (line.strip() == '' or line.startswith('#') or line.startswith('|')):
+                    # Remove lines until we hit a natural break
+                    while truncated_lines and not (
+                        truncated_lines[-1].strip() == '' or 
+                        truncated_lines[-1].startswith('#') or 
+                        truncated_lines[-1].startswith('|') or
+                        truncated_lines[-1].startswith('-') or
+                        truncated_lines[-1].startswith('*')
+                    ):
+                        truncated_lines.pop()
+            break
+        
+        truncated_lines.append(line)
+        current_length += line_length
+    
+    # Add truncation notice
+    result = '\n'.join(truncated_lines)
+    result += f"\n\n---\n**[CONTENT TRUNCATED]**\nShowing {len(result):,} of {len(content):,} characters.\nUse smaller page ranges (e.g., 3-5 pages) for full content without truncation.\n---"
+    
+    return result
+
+
 def _estimate_section_length(heading_level: int) -> int:
     """Estimate how many pages a section might span based on heading level."""
     # Higher level headings (H1) tend to have longer sections
@@ -1598,7 +1658,8 @@ def _generate_chunking_suggestions(sections: list) -> list[dict[str, Any]]:
         section_pages = section["estimated_end_page"] - section["start_page"] + 1
         
         # If adding this section would make chunk too large, finalize current chunk
-        if current_chunk_pages + section_pages > 15 and chunk_sections:
+        # Use smaller chunks (8 pages) to prevent MCP token limit issues
+        if current_chunk_pages + section_pages > 8 and chunk_sections:
             suggestions.append({
                 "chunk_number": len(suggestions) + 1,
                 "page_range": f"{chunk_start}-{chunk_sections[-1]['estimated_end_page']}",
@@ -1761,13 +1822,15 @@ def _get_processing_recommendation(
             "Consider using recommended workflow for better performance."
         )
         recommendation["suggested_workflow"] = [
-            "1. First: Call with summary_only=true to get document overview",
-            "2. Then: Use page_range to process specific sections (e.g., '1-10', '20-30')",
-            "3. Alternative: Process in chunks of 10-15 pages to avoid response limits"
+            "1. First: Call with summary_only=true to get document overview and TOC",
+            "2. Then: Use page_range to process specific sections (e.g., '1-5', '6-10', '15-20')",
+            "3. Recommended: Use 3-8 page chunks to stay under 25k token MCP limit",
+            "4. The tool auto-truncates if content is too large, but smaller ranges work better"
         ]
         recommendation["warnings"] = [
-            "Full document processing may hit 25k token response limit",
-            "Large responses may be slow and consume significant resources"
+            "Page ranges >8 pages may hit 25k token response limit and get truncated",
+            "Use smaller page ranges (3-5 pages) for dense content documents",
+            "Auto-truncation preserves structure but loses content completeness"
         ]
     
     # Medium document recommendations