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Product Requirements Document: MCP Documentation Server

Version: 1.0 Date: September 18, 2025 Author: Product Team

Executive Summary

The MCP Documentation Server enables efficient LLM interaction with large, structured documentation projects by providing hierarchical, content-aware access to AsciiDoc and Markdown documents instead of traditional file-based access.

Problem Statement

Current Challenge

  • Large Document Problem: LLMs struggle with large documentation files due to token limitations and context window constraints
  • Poor Overview: LLMs cannot efficiently navigate or understand the structure of complex documentation projects
  • Inefficient Token Usage: File-based access forces loading entire documents even when only specific sections are needed
  • Limited Granular Control: No structured way to modify specific parts of documents without manual file editing

Impact

  • Developers and architects waste time manually chunking and organizing documentation for LLM consumption
  • Large projects (like comprehensive arc42 documentation) become practically unusable with LLMs
  • Documentation maintenance becomes inefficient and error-prone

Target Audience

Primary Users

  • Software Developers working with LLMs on code documentation
  • Software Architects maintaining technical documentation with LLM assistance
  • Documentation Engineers managing large documentation projects

Use Cases

  • Code documentation analysis and maintenance
  • Architecture documentation updates (arc42, technical specs)
  • Large project documentation navigation and editing
  • Requirements documentation management

Solution Overview

Core Value Proposition

Enable structured, efficient LLM interaction with large documentation projects through intelligent document parsing, hierarchical access, and granular manipulation capabilities.

Key Benefits

  • Reduced Token Usage: Access only relevant document sections
  • Better Context Understanding: Maintain document structure and hierarchy
  • Efficient Navigation: Quick access to specific content without full document loading
  • Precise Editing: Granular content modification with visual feedback

Functional Requirements

Must-Have Features

1. Document Processing

  • Multi-file Project Support: Handle projects with multiple AsciiDoc/Markdown files
  • Include Resolution: Parse and maintain include structures while preserving relationships
  • Structure Analysis: Extract hierarchical document structure (chapters, sections, subsections)
  • Content Indexing: Create searchable index of document content

2. Hierarchical Navigation API

get_structure(max_depth: int) → Table of Contents
get_section(path: "chapter.subchapter") → Content
get_sections(level: int) → All sections at level

3. Content-Based Access API

get_elements(type: "diagram|table|code|list") → Filtered content
search_content(query: string) → Matching sections
get_summary(scope: "chapter.subchapter") → AI-generated summary

4. Content Manipulation API

update_section(path: string, content: string) → Success/Error
insert_at(path: string, position: "before|after|append", content: string)
replace_element(path: string, element_index: int, content: string)

5. Meta-Information API

get_metadata(path?: string) → File info, word count, last modified
get_dependencies() → Include-tree, cross-references
validate_structure() → Consistency check

6. File System Integration

  • Direct File Modification: Write changes directly to source files
  • Version Control Compatibility: Maintain compatibility with Git workflows
  • Atomic Operations: Ensure file consistency during modifications

7. Web Interface

  • Document Visualization: Display processed document structure
  • Real-time Diff Display: Show red/green diffs after each modification
  • Navigation Interface: Browse document hierarchy via web UI

Nice-to-Have Features

  • Multiple Format Support: Support for additional markup formats
  • Advanced Search: Full-text search with relevance scoring
  • Change History: Track modification history within sessions
  • Export Options: Export processed content in different formats

Non-Functional Requirements

Performance

  • Preprocessing Acceptable: Initial indexing/parsing time is acceptable for startup
  • Memory Efficiency: Handle ~600 pages (6 × 100-page arc42 docs) efficiently
  • Response Time: API calls should respond within 2 seconds for typical operations

Scalability

  • Project Size: Support projects up to 600 pages of documentation
  • Concurrent Access: Handle multiple LLM clients accessing the same project
  • File Watching: Detect external file changes and update index accordingly

Reliability

  • Data Integrity: Ensure no data loss during file modifications
  • Error Handling: Graceful handling of malformed documents or file system errors
  • Recovery: Ability to recover from partial operations

Usability

  • MCP Compliance: Full compliance with Model Context Protocol standards
  • Clear Error Messages: Descriptive error responses for failed operations
  • Documentation: Comprehensive API documentation and usage examples

Technical Architecture Requirements

Core Components

  1. Document Parser Engine: AsciiDoc/Markdown parsing with include resolution
  2. Structure Indexer: Hierarchical content mapping and navigation
  3. MCP Server Interface: Protocol-compliant API endpoint
  4. File System Handler: Safe file modification with backup capabilities
  5. Web Server: Simple HTTP server for visualization interface
  6. Diff Engine: Change detection and visualization

Integration Requirements

  • MCP Protocol: Full Model Context Protocol compliance
  • File System: Direct file system access for reading/writing
  • Git Integration: Compatible with standard Git workflows
  • Web Standards: Standard HTTP/WebSocket for web interface

Success Metrics

Primary KPIs

  • Token Usage Reduction: Measure reduced token consumption compared to full-file access
  • Navigation Efficiency: Time to locate specific content within large documents
  • Modification Accuracy: Success rate of granular content modifications

Secondary Metrics

  • User Adoption: Number of projects using the MCP server
  • Error Rate: Frequency of failed operations or data corruption
  • Performance: Average response times for different operation types

Constraints and Assumptions

Technical Constraints

  • Must work with existing AsciiDoc/Markdown toolchains
  • Files must remain human-readable and editable
  • No database dependencies (file-system based)

Organizational Constraints

  • Integration with existing developer workflows
  • Version control system compatibility required
  • No external service dependencies

Assumptions

  • Documents follow standard AsciiDoc/Markdown conventions
  • Include files are accessible within project directory
  • Users have file system write permissions
  • Git or similar VCS is used for change tracking

Timeline and Milestones

Phase 1: Core Engine (4-6 weeks)

  • Document parsing and structure extraction
  • Basic hierarchical navigation API
  • File modification capabilities

Phase 2: MCP Integration (2-3 weeks)

  • Full MCP protocol implementation
  • API refinement and testing
  • Error handling and validation

Phase 3: Web Interface (2-3 weeks)

  • Web-based document visualization
  • Diff display functionality
  • Basic navigation interface

Phase 4: Polish and Optimization (2-3 weeks)

  • Performance optimization
  • Advanced features implementation
  • Documentation and testing

Risk Assessment

High Risk

  • Include Resolution Complexity: Circular includes or complex dependency chains
  • File Corruption: Risk during concurrent modifications
  • Performance: Large document processing efficiency

Medium Risk

  • Format Variations: Different AsciiDoc/Markdown dialects
  • MCP Protocol Evolution: Changes to protocol specifications
  • Cross-platform Compatibility: File system differences

Mitigation Strategies

  • Comprehensive testing with real-world documentation projects
  • Backup mechanisms for file modifications
  • Incremental development with early user feedback

Appendix

Related Technologies

  • Model Context Protocol (MCP): Protocol specification for LLM tool integration
  • AsciiDoc: Lightweight markup language for technical documentation
  • Markdown: Popular markup language for documentation
  • arc42: Template for software architecture documentation

Reference Projects

  • Existing documentation projects that would benefit from this tool
  • Current MCP server implementations for reference
  • Document processing libraries and tools