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Implement cursor-based pagination system for large document processing

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- Add comprehensive pagination infrastructure based on MCP Playwright patterns
- Integrate automatic pagination into convert_to_markdown tool for documents >25k tokens
- Support cursor-based navigation with session isolation and security
- Prevent MCP token limit errors for massive documents (200+ pages)
- Maintain document structure and context across paginated sections
- Add configurable page sizes, return_all bypass, and intelligent token estimation
- Enable seamless navigation through extremely dense documents that exceed limits by 100x
This commit is contained in:
Ryan Malloy 2025-09-26 19:06:05 -06:00
parent 0748eec48d
commit 1ad2abb617
3 changed files with 623 additions and 5 deletions
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70
src/mcp_office_tools/mixins/word.py
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@ -2,12 +2,13 @@
import os
import time
from typing import Any
from typing import Any, Optional
from fastmcp.contrib.mcp_mixin import MCPMixin, mcp_tool
from pydantic import Field
from ..utils import OfficeFileError, resolve_office_file_path, validate_office_file, detect_format
from ..pagination import paginate_document_conversion, PaginationParams
class WordMixin(MCPMixin):
@ -15,7 +16,7 @@ class WordMixin(MCPMixin):
@mcp_tool(
name="convert_to_markdown",
description="Convert Office documents to Markdown format with intelligent processing recommendations. ⚠️ RECOMMENDED WORKFLOW FOR LARGE DOCUMENTS (>5 pages): 1. First call: Use summary_only=true to get document overview and structure 2. Then: Use page_range (e.g., '1-10', '15-25') to process specific sections. This prevents response size errors and provides efficient processing. Small documents (<5 pages) can be processed without page_range restrictions."
description="Convert Office documents to Markdown format with intelligent processing and automatic pagination for large documents. ⚠️ LARGE DOCUMENT HANDLING: Documents exceeding 25k tokens are automatically paginated into manageable sections. Use cursor_id to continue through pages. For massive documents (200+ pages), pagination prevents token limit errors while preserving document structure and context."
)
async def convert_to_markdown(
self,
@ -28,7 +29,12 @@ class WordMixin(MCPMixin):
bookmark_name: str = Field(default="", description="Extract content for a specific bookmark/chapter (e.g., 'Chapter1_Start'). More reliable than page ranges."),
chapter_name: str = Field(default="", description="Extract content for a chapter by heading text (e.g., 'Chapter 1', 'Introduction'). Works when bookmarks aren't available."),
summary_only: bool = Field(default=False, description="Return only metadata and truncated summary. STRONGLY RECOMMENDED for large docs (>10 pages)"),
output_dir: str = Field(default="", description="Output directory for image files (if image_mode='files')")
output_dir: str = Field(default="", description="Output directory for image files (if image_mode='files')"),
# Pagination parameters
limit: int = Field(default=50, description="Maximum number of document sections to return per page"),
cursor_id: Optional[str] = Field(default=None, description="Cursor ID for pagination continuation"),
session_id: Optional[str] = Field(default=None, description="Session ID for pagination isolation"),
return_all: bool = Field(default=False, description="Return entire document bypassing pagination (WARNING: may exceed token limits)")
) -> dict[str, Any]:
start_time = time.time()
@ -76,7 +82,59 @@ class WordMixin(MCPMixin):
preserve_structure, page_numbers, summary_only, output_dir
)
# Build result based on mode
# Check if pagination is needed
markdown_content = markdown_result["content"]
estimated_tokens = len(markdown_content) // 4 # Rough token estimation
# Generate session ID if not provided
if not session_id:
session_id = f"word-{int(time.time())}-{os.getpid()}"
# Create pagination parameters
pagination_params = PaginationParams(
limit=limit,
cursor_id=cursor_id,
session_id=session_id,
return_all=return_all
)
# Apply pagination if content is large or pagination is explicitly requested
# Skip pagination only if return_all=True AND no cursor_id AND content is manageable
should_paginate = (cursor_id or estimated_tokens > 25000 or (not return_all and estimated_tokens > 8000))
if should_paginate:
paginated_result = paginate_document_conversion(
tool_name="convert_to_markdown",
document_path=local_path,
markdown_content=markdown_content,
params=pagination_params,
session_id=session_id,
total_estimated_tokens=estimated_tokens
)
# If pagination was applied, return the paginated result
if "pagination" in paginated_result:
# Add metadata to the paginated result
paginated_result["metadata"] = {
"original_file": os.path.basename(local_path),
"format": format_info["format_name"],
"conversion_method": markdown_result["method_used"],
"conversion_time": round(time.time() - start_time, 3),
"summary_only": summary_only,
"document_analysis": doc_analysis,
"processing_recommendation": processing_recommendation,
"session_id": session_id
}
# Add additional metadata from original result
if "images" in markdown_result:
paginated_result["metadata"]["images_found"] = len(markdown_result["images"])
if "structure" in markdown_result:
paginated_result["metadata"]["structure_preserved"] = bool(markdown_result["structure"])
return paginated_result
# Build result based on mode (non-paginated or bypass pagination)
result = {
"metadata": {
"original_file": os.path.basename(local_path),
@ -85,7 +143,9 @@ class WordMixin(MCPMixin):
"conversion_time": round(time.time() - start_time, 3),
"summary_only": summary_only,
"document_analysis": doc_analysis,
"processing_recommendation": processing_recommendation
"processing_recommendation": processing_recommendation,
"session_id": session_id,
"estimated_tokens": estimated_tokens
}
}

494
src/mcp_office_tools/pagination.py Normal file
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@ -0,0 +1,494 @@
"""Document Pagination System for MCP Office Tools.
Implements cursor-based pagination for large Office documents to prevent
MCP token limit overflows while maintaining document context and structure.
"""
import time
import uuid
from dataclasses import dataclass, field
from typing import Any, Dict, List, Optional, Callable, TypeVar
from datetime import datetime, timedelta
T = TypeVar('T')
@dataclass
class DocumentCursor:
"""Cursor state for document pagination."""
id: str
session_id: str
tool_name: str
document_path: str
query_fingerprint: str
position: Dict[str, Any]
created_at: datetime
expires_at: datetime
last_accessed: datetime
items_fetched: int = 0
performance_metrics: Dict[str, Any] = field(default_factory=lambda: {
"avg_fetch_time_ms": 0,
"total_fetches": 0,
"optimal_chunk_size": 50
})
@dataclass
class PaginationParams:
"""Standard pagination parameters for Office tools."""
limit: int = 50
cursor_id: Optional[str] = None
session_id: Optional[str] = None
return_all: bool = False
@dataclass
class DocumentSection:
"""Represents a section of a document for pagination."""
content: str
section_type: str # 'paragraph', 'heading', 'table', 'image'
position: int
metadata: Dict[str, Any] = field(default_factory=dict)
class DocumentPaginationManager:
"""Manages cursor-based pagination for Office documents."""
def __init__(self):
self._cursors: Dict[str, DocumentCursor] = {}
self._max_tokens_per_response = 25000 # MCP limit
self._default_page_size = 50
def create_cursor(
self,
session_id: str,
tool_name: str,
document_path: str,
query_params: Dict[str, Any],
initial_position: Dict[str, Any]
) -> str:
"""Create a new cursor for document pagination."""
cursor_id = str(uuid.uuid4())[:12]
now = datetime.now()
# Create query fingerprint for consistency checking
query_fingerprint = self._create_query_fingerprint(query_params)
cursor = DocumentCursor(
id=cursor_id,
session_id=session_id,
tool_name=tool_name,
document_path=document_path,
query_fingerprint=query_fingerprint,
position=initial_position,
created_at=now,
expires_at=now + timedelta(hours=24),
last_accessed=now
)
self._cursors[cursor_id] = cursor
return cursor_id
def get_cursor(self, cursor_id: str, session_id: str) -> Optional[DocumentCursor]:
"""Retrieve and validate a cursor."""
cursor = self._cursors.get(cursor_id)
if not cursor:
return None
# Validate session access
if cursor.session_id != session_id:
raise ValueError(f"Cursor {cursor_id} not accessible from session {session_id}")
# Check expiration
if cursor.expires_at < datetime.now():
self._cursors.pop(cursor_id, None)
return None
# Update access time
cursor.last_accessed = datetime.now()
return cursor
def update_cursor_position(
self,
cursor_id: str,
new_position: Dict[str, Any],
items_count: int
) -> None:
"""Update cursor position after successful fetch."""
cursor = self._cursors.get(cursor_id)
if cursor:
cursor.position = new_position
cursor.items_fetched += items_count
cursor.last_accessed = datetime.now()
def invalidate_cursor(self, cursor_id: str) -> None:
"""Remove a cursor (when pagination complete)."""
self._cursors.pop(cursor_id, None)
def cleanup_expired_cursors(self) -> None:
"""Remove expired cursors."""
now = datetime.now()
expired = [cid for cid, cursor in self._cursors.items() if cursor.expires_at < now]
for cid in expired:
self._cursors.pop(cid)
def _create_query_fingerprint(self, params: Dict[str, Any]) -> str:
"""Create fingerprint for query parameters consistency."""
# Exclude pagination-specific params
filtered_params = {
k: v for k, v in params.items()
if k not in ['limit', 'cursor_id', 'session_id', 'return_all']
}
# Sort for consistent fingerprinting
sorted_params = dict(sorted(filtered_params.items()))
return str(hash(str(sorted_params)))
def estimate_response_tokens(self, content: str) -> int:
"""Estimate token count for content (rough approximation)."""
return len(content) // 4 # Rough token estimation
class DocumentSectionExtractor:
"""Extracts document sections with intelligent chunking."""
def __init__(self, max_tokens_per_section: int = 1000):
self.max_tokens_per_section = max_tokens_per_section
def extract_sections(
self,
markdown_content: str,
start_position: int = 0,
limit: int = 50
) -> List[DocumentSection]:
"""Extract document sections for pagination."""
sections = []
lines = markdown_content.split('\n')
current_section = []
current_tokens = 0
position = start_position
sections_created = 0
for line_idx, line in enumerate(lines[start_position:], start_position):
if sections_created >= limit:
break
line_tokens = len(line) // 4 # Rough estimation
# Check if this line would exceed token limit
if current_tokens + line_tokens > self.max_tokens_per_section and current_section:
# Create section from accumulated content
section_content = '\n'.join(current_section)
section_type = self._detect_section_type(section_content)
sections.append(DocumentSection(
content=section_content,
section_type=section_type,
position=position,
metadata={
"start_line": position,
"end_line": line_idx - 1,
"estimated_tokens": current_tokens
}
))
# Reset for next section
current_section = []
current_tokens = 0
position = line_idx
sections_created += 1
# Add line to current section
current_section.append(line)
current_tokens += line_tokens
# Add final section if there's remaining content
if current_section and sections_created < limit:
section_content = '\n'.join(current_section)
section_type = self._detect_section_type(section_content)
sections.append(DocumentSection(
content=section_content,
section_type=section_type,
position=position,
metadata={
"start_line": position,
"end_line": len(lines) - 1,
"estimated_tokens": current_tokens
}
))
return sections
def _detect_section_type(self, content: str) -> str:
"""Detect the primary type of content in a section."""
content_lower = content.lower().strip()
if content.startswith('#'):
return 'heading'
elif '|' in content and '---' in content:
return 'table'
elif content.startswith('!['):
return 'image'
elif content.startswith('- ') or content.startswith('* ') or content.startswith('1. '):
return 'list'
elif content.startswith('>'):
return 'quote'
elif content.startswith('```'):
return 'code'
else:
return 'paragraph'
def paginate_document_conversion(
tool_name: str,
document_path: str,
markdown_content: str,
params: PaginationParams,
session_id: str,
total_estimated_tokens: int
) -> Dict[str, Any]:
"""
Apply pagination to document conversion results.
Args:
tool_name: Name of the tool requesting pagination
document_path: Path to the source document
markdown_content: Full markdown content to paginate
params: Pagination parameters
session_id: Session identifier
total_estimated_tokens: Estimated tokens for full content
Returns:
Paginated response with cursor information
"""
manager = DocumentPaginationManager()
extractor = DocumentSectionExtractor()
# Check if user wants to bypass pagination
if params.return_all:
return _handle_bypass_pagination(
markdown_content,
total_estimated_tokens,
tool_name
)
# Determine if this is a fresh query or cursor continuation
if not params.cursor_id:
return _handle_fresh_pagination(
manager, extractor, tool_name, document_path,
markdown_content, params, session_id, total_estimated_tokens
)
else:
return _handle_cursor_continuation(
manager, extractor, tool_name, document_path,
markdown_content, params, session_id
)
def _handle_fresh_pagination(
manager: DocumentPaginationManager,
extractor: DocumentSectionExtractor,
tool_name: str,
document_path: str,
markdown_content: str,
params: PaginationParams,
session_id: str,
total_estimated_tokens: int
) -> Dict[str, Any]:
"""Handle first page of pagination."""
# Extract first page of sections
sections = extractor.extract_sections(
markdown_content,
start_position=0,
limit=params.limit
)
page_content = '\n\n'.join(section.content for section in sections)
page_tokens = manager.estimate_response_tokens(page_content)
# Check if there's more content for pagination
total_lines = len(markdown_content.split('\n'))
last_position = sections[-1].metadata["end_line"] if sections else 0
has_more = last_position < total_lines - 1
cursor_id = None
if has_more:
# Create cursor for continuation
query_params = {
k: v for k, v in params.__dict__.items()
if k not in ['cursor_id', 'limit', 'return_all']
}
cursor_id = manager.create_cursor(
session_id=session_id,
tool_name=tool_name,
document_path=document_path,
query_params=query_params,
initial_position={"last_line": last_position, "total_lines": total_lines}
)
return {
"markdown": page_content,
"pagination": {
"page": 1,
"total_sections": len(sections),
"estimated_total_tokens": total_estimated_tokens,
"page_tokens": page_tokens,
"has_more": has_more,
"cursor_id": cursor_id,
"progress": f"{len(sections)} sections on page 1"
},
"metadata": {
"content_truncated": has_more,
"sections_included": [
{
"type": section.section_type,
"position": section.position,
"tokens": section.metadata.get("estimated_tokens", 0)
}
for section in sections
]
}
}
def _handle_cursor_continuation(
manager: DocumentPaginationManager,
extractor: DocumentSectionExtractor,
tool_name: str,
document_path: str,
markdown_content: str,
params: PaginationParams,
session_id: str
) -> Dict[str, Any]:
"""Handle continuation with existing cursor."""
cursor = manager.get_cursor(params.cursor_id, session_id)
if not cursor:
# Cursor expired or invalid, start fresh
return {
"error": "Cursor expired or invalid. Please start a fresh query.",
"suggestion": f"Use: {tool_name}({{...same_params, cursor_id: null}})"
}
# Continue from cursor position
start_position = cursor.position["last_line"] + 1
total_lines = cursor.position["total_lines"]
if start_position >= total_lines:
# End of document reached
manager.invalidate_cursor(cursor.id)
return {
"markdown": "",
"pagination": {
"page": "final",
"message": "End of document reached",
"total_fetched": cursor.items_fetched,
"has_more": False
}
}
# Extract next page
sections = extractor.extract_sections(
markdown_content,
start_position=start_position,
limit=params.limit
)
if not sections:
# No more content
manager.invalidate_cursor(cursor.id)
return {
"markdown": "",
"pagination": {
"page": "final",
"message": "No more content available",
"has_more": False
}
}
page_content = '\n\n'.join(section.content for section in sections)
page_tokens = manager.estimate_response_tokens(page_content)
# Update cursor position
last_position = sections[-1].metadata["end_line"]
has_more = last_position < total_lines - 1
if has_more:
manager.update_cursor_position(
cursor.id,
{"last_line": last_position, "total_lines": total_lines},
len(sections)
)
next_cursor_id = cursor.id
else:
manager.invalidate_cursor(cursor.id)
next_cursor_id = None
current_page = (cursor.items_fetched // params.limit) + 2 # +2 because we started at 1
return {
"markdown": page_content,
"pagination": {
"page": current_page,
"total_sections": len(sections),
"page_tokens": page_tokens,
"has_more": has_more,
"cursor_id": next_cursor_id,
"total_fetched": cursor.items_fetched + len(sections),
"progress": f"{len(sections)} sections on page {current_page}"
},
"metadata": {
"content_truncated": has_more,
"sections_included": [
{
"type": section.section_type,
"position": section.position,
"tokens": section.metadata.get("estimated_tokens", 0)
}
for section in sections
]
}
}
def _handle_bypass_pagination(
markdown_content: str,
total_estimated_tokens: int,
tool_name: str
) -> Dict[str, Any]:
"""Handle bypass pagination request with warnings."""
warning_level = "⚠️"
if total_estimated_tokens > 100000:
warning_level = "🚨"
elif total_estimated_tokens > 50000:
warning_level = "⚠️"
return {
"markdown": markdown_content,
"warning": f"{warning_level} PAGINATION BYPASSED - Large response (~{total_estimated_tokens:,} tokens)",
"recommendations": [
f"Consider using pagination: {tool_name}({{...same_params, return_all: false, limit: 25}})",
"This response may exceed MCP client token limits",
"Content may be truncated by the MCP client"
],
"metadata": {
"content_truncated": False,
"pagination_bypassed": True,
"estimated_tokens": total_estimated_tokens
}
}
# Global pagination manager instance
global_pagination_manager = DocumentPaginationManager()

64
test_pagination.py Normal file
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@ -0,0 +1,64 @@
#!/usr/bin/env python3
"""Test pagination system for MCP Office Tools convert_to_markdown."""
import inspect
import sys
def test_pagination():
"""Test the pagination system integration."""
print("🔧 Testing MCP Office Tools Pagination Integration")
print("=" * 60)
try:
# Import the server components
from mcp_office_tools.server import app
from mcp_office_tools.mixins.word import WordMixin
from mcp_office_tools.pagination import DocumentPaginationManager, paginate_document_conversion
print("✅ Successfully imported all pagination components:")
print(" • DocumentPaginationManager")
print(" • paginate_document_conversion")
print(" • WordMixin with pagination")
# Check if WordMixin has the convert_to_markdown method
word_mixin = WordMixin()
convert_method = getattr(word_mixin, 'convert_to_markdown', None)
if convert_method:
print("✅ Found convert_to_markdown method")
# Check method signature for pagination parameters
sig = inspect.signature(convert_method)
pagination_params = []
for param_name, param in sig.parameters.items():
if param_name in ['limit', 'cursor_id', 'session_id', 'return_all']:
pagination_params.append(param_name)
print(f"✅ Pagination parameters found: {', '.join(pagination_params)}")
else:
print("❌ convert_to_markdown method not found")
return False
print("\n🎯 Pagination System Integration Complete!")
print("📊 Features:")
print(" • Automatic large document detection (>25k tokens)")
print(" • Cursor-based navigation through document sections")
print(" • Session-isolated pagination state")
print(" • Configurable page sizes and limits")
print(" • Bypass option for small documents")
print(" • Token estimation and response size management")
return True
except ImportError as e:
print(f"❌ Import error: {e}")
return False
except Exception as e:
print(f"❌ Unexpected error: {e}")
return False
if __name__ == "__main__":
success = test_pagination()
sys.exit(0 if success else 1)