🏗️ Core MCP Server Implementation Workflow

Strategy: MVP (Minimum Viable Product) Primary Persona: Architect Integration: Sequential Analysis Timeline: 2-3 weeks

🎯 MVP Scope Definition

Core Requirements

  • 4 MCP Tools: process_document, query_documents, get_document_info, list_sessions

  • uvx Compatibility: One-command installation and execution

  • Basic Document Processing: PDF text extraction + simple OCR

  • Simple RAG: Vector embeddings + semantic search + LLM generation

  • Session Management: Multi-turn conversation support

Success Criteria

  • uvx mcp-academic-rag-server installs and runs

  • All 4 MCP tools work in Claude Desktop

  • Can process a PDF and query it successfully

  • Setup takes < 5 minutes for new users

  • Memory usage < 500MB for normal operations

📋 Phase 1: Architecture Foundation (Week 1)

1.1 System Architecture Design

Duration: 8 hours | Dependencies: None

Core Architecture Principles

┌─────────────────┐    ┌──────────────────┐    ┌─────────────────┐
│ MCP Protocol    │────│ Core Processing  │────│ Simple Storage  │
│ - 4 Tools       │    │ - Document Proc  │    │ - Local Files   │
│ - JSON Schema   │    │ - OCR Engine     │    │ - FAISS Vectors │
│ - Error Handling│    │ - RAG Pipeline   │    │ - JSON Sessions │
└─────────────────┘    └──────────────────┘    └─────────────────┘

Implementation Tasks

  • Define clean interfaces between MCP layer and processing layer

  • Design error handling strategy for MCP tool failures

  • Plan configuration management (single JSON file approach)

  • Establish logging strategy (simple structured logging, no complex monitoring)

  • Document API contracts for each MCP tool

Deliverables

  • Architecture diagram with component boundaries

  • Interface definitions for core components

  • Error handling and logging strategy document

1.2 Dependency Simplification

Duration: 6 hours | Dependencies: Architecture design

Target Dependency Profile

Core (≤15 packages):
- mcp >= 1.0.0                    # MCP protocol
- PyPDF2 >= 3.0.0                 # PDF processing
- pytesseract >= 0.3.10           # OCR
- haystack-ai >= 2.0.0            # RAG pipeline
- sentence-transformers >= 2.2.0   # Embeddings
- openai >= 1.0.0                 # LLM API
- faiss-cpu >= 1.7.0              # Vector storage
- requests >= 2.31.0              # HTTP client
- aiohttp >= 3.9.0                # Async HTTP
- python-dotenv >= 1.0.0          # Environment config
- pydantic >= 2.0.0               # Data validation
- loguru >= 0.7.0                 # Logging
- jsonschema >= 4.0.0             # JSON validation

Implementation Tasks

  • Audit current dependencies (70+ packages) for removal candidates

  • Identify essential vs. nice-to-have dependencies

  • Replace complex dependencies with simpler alternatives

    • Milvus → FAISS (local vector storage)

    • Redis → In-memory Python cache

    • Complex web framework → Minimal Flask (if needed)

  • Update pyproject.toml with simplified dependency list

  • Test minimal installation to verify all functionality works

Risk Mitigation

  • Risk: Breaking existing functionality

  • Mitigation: Create feature compatibility matrix, test each component individually

1.3 uvx Package Structure

Duration: 4 hours | Dependencies: Dependency simplification

Package Configuration

  • Configure pyproject.toml for uvx compatibility

  • Set up entry points for MCP server (mcp-academic-rag-server)

  • Add optional dependencies for enhanced features

  • Configure build system for clean package distribution

  • Test uvx installation locally and in clean environments

Entry Point Configuration

[project.scripts]
mcp-academic-rag-server = "mcp_server:main"
academic-rag-process = "cli.document_cli:main" 
academic-rag-chat = "cli.chat_cli:main"

📋 Phase 2: Core Implementation (Week 2)

2.1 MCP Protocol Layer

Duration: 12 hours | Dependencies: Architecture foundation

MCP Server Implementation

  • Implement MCP server initialization with proper capabilities

  • Define tool schemas with comprehensive JSON validation

  • Implement tool routing with error handling and logging

  • Add async support for non-blocking operations

  • Implement health check mechanism

Tool Implementation Priority

  1. process_document (4 hours)

    async def process_document(arguments: Dict[str, Any]) -> List[types.TextContent]:
    # Validate input parameters
    # Process document through pipeline
    # Return structured response with document ID and metadata

  2. query_documents (4 hours)

    async def query_documents(arguments: Dict[str, Any]) -> List[types.TextContent]:
    # Parse query and session context
    # Execute RAG pipeline
    # Return answer with source citations

  3. get_document_info (2 hours)

    async def get_document_info(arguments: Dict[str, Any]) -> List[types.TextContent]:
    # Retrieve document metadata
    # Return processing status and statistics

  4. list_sessions (2 hours)

    async def list_sessions(arguments: Dict[str, Any]) -> List[types.TextContent]:
    # List all active sessions
    # Return session metadata and statistics

2.2 Document Processing Pipeline

Duration: 10 hours | Dependencies: MCP layer

Processing Components

  1. PDF Text Extraction (2 hours)

    • Direct text extraction from PDF files

    • Handle encrypted/protected PDFs gracefully

    • Extract metadata (title, author, page count)

  2. OCR Processing (3 hours)

    • Image-based text recognition using Tesseract

    • Support for PNG, JPG, TIFF formats

    • Language detection and multi-language support

  3. Text Chunking (2 hours)

    • Intelligent document segmentation

    • Configurable chunk sizes (default: 1000 chars, 200 overlap)

    • Preserve document structure context

  4. Pipeline Orchestration (3 hours)

    • Async pipeline execution

    • Error recovery and partial processing support

    • Progress tracking and status updates

2.3 Simple RAG System

Duration: 8 hours | Dependencies: Document processing

RAG Components

  1. Vector Storage (3 hours)

    • FAISS-based local vector storage

    • Efficient similarity search implementation

    • Metadata filtering and retrieval

  2. Query Processing (3 hours)

    • Query embedding generation

    • Semantic similarity search

    • Context compilation for LLM

  3. Response Generation (2 hours)

    • LLM integration (OpenAI API)

    • Prompt template system

    • Source citation extraction

📋 Phase 3: Integration & Optimization (Week 3)

3.1 Session Management

Duration: 6 hours | Dependencies: RAG system

Session Features

  • Multi-turn conversation support with context preservation

  • Session persistence using simple JSON storage

  • Session cleanup and memory management

  • Conversation history with message threading

3.2 Configuration & Setup

Duration: 4 hours | Dependencies: Core implementation

Configuration System

  • Single configuration file approach (config.json)

  • Environment variable support (.env file)

  • Configuration validation with helpful error messages

  • Default configuration that works out-of-box

Installation Flow

  • uvx installation testing on multiple platforms

  • Dependency resolution verification

  • First-run setup guidance

  • Health check command for troubleshooting

3.3 Error Handling & Logging

Duration: 4 hours | Dependencies: All components

Robust Error Handling

  • Graceful degradation when components fail

  • Clear error messages for users

  • Retry logic for transient failures

  • Fallback mechanisms for critical operations

Logging Strategy

  • Structured logging with correlation IDs

  • Configurable log levels (DEBUG, INFO, WARN, ERROR)

  • Log rotation to prevent disk usage issues

  • No sensitive data logging (API keys, document content)

🧪 Quality Gates

Phase 1 Gates

  • Architecture review completed and approved

  • Dependency count reduced to <20 packages

  • uvx package structure validated

Phase 2 Gates

  • All 4 MCP tools implemented and unit tested

  • Document processing handles PDF and images

  • RAG system returns relevant results

Phase 3 Gates

  • End-to-end workflow tested (document upload → query → response)

  • Installation via uvx works on clean environments

  • Memory usage stays under 500MB during normal operation

  • Error handling provides actionable feedback

📊 Success Metrics

Technical Metrics

  • Installation Time: < 5 minutes from uvx install to first query

  • Memory Usage: < 500MB for processing typical academic papers (10-20 pages)

  • Response Time: < 10 seconds for document processing, < 5 seconds for queries

  • Error Rate: < 5% for common operations (PDF processing, basic queries)

User Experience Metrics

  • Setup Complexity: New user can complete setup without reading detailed documentation

  • Feature Discovery: All 4 MCP tools visible and functional in MCP client

  • Error Clarity: Error messages provide specific, actionable guidance

  • Documentation Quality: Single-page setup guide sufficient for 80% of users

Business Metrics

  • Adoption Rate: Time from project discovery to first successful query

  • User Satisfaction: Feedback on ease of use and reliability

  • Maintenance Overhead: Time required for ongoing maintenance and support

🚨 Risk Assessment & Mitigation

Technical Risks

High Risk: uvx Compatibility Issues

  • Impact: Users cannot install or run the server

  • Probability: Medium (new packaging system)

  • Mitigation: Extensive testing on multiple platforms, fallback installation methods

Medium Risk: Dependency Conflicts

  • Impact: Installation failures or runtime errors

  • Probability: Low (simplified dependency list)

  • Mitigation: Lock dependency versions, provide virtual environment guidance

Medium Risk: Performance Degradation

  • Impact: Slow response times, high memory usage

  • Probability: Medium (simplifying from complex system)

  • Mitigation: Performance testing, memory profiling, optimization iteration

Business Risks

Low Risk: Feature Gaps

  • Impact: Users missing expected functionality from complex version

  • Probability: Low (well-defined scope reduction)

  • Mitigation: Clear communication about focused scope, migration guide for power users

🔄 Iteration Strategy

MVP Launch Criteria

  1. Core Functionality: All 4 MCP tools working reliably

  2. Easy Installation: uvx install + API key setup < 5 minutes

  3. Basic Performance: Handles typical academic papers without issues

  4. Error Recovery: Graceful handling of common failure scenarios

Post-MVP Iterations

  1. Performance Optimization: Memory usage reduction, speed improvements

  2. Enhanced Error Handling: Better diagnostic messages, recovery options

  3. Configuration Flexibility: More customization options while maintaining simplicity

  4. Additional Document Formats: Support for more file types if requested

Feedback Integration

  • Usage Analytics: Track which features are most/least used

  • Error Monitoring: Identify common failure patterns

  • User Feedback: Direct feedback on pain points and desired improvements

  • Performance Metrics: Monitor real-world performance characteristics

🎯 Next Steps

Immediate Actions (Week 1)

  1. Architecture Review - Validate component boundaries and interfaces

  2. Dependency Audit - Create detailed plan for dependency reduction

  3. uvx Testing - Set up local testing environment for uvx compatibility

Implementation Priorities (Week 2)

  1. MCP Tools - Implement core tools with comprehensive error handling

  2. Document Processing - Build reliable PDF and OCR processing pipeline

  3. RAG Integration - Connect processing to query/response system

Validation & Launch (Week 3)

  1. End-to-End Testing - Complete workflow validation

  2. Performance Optimization - Memory usage and response time tuning

  3. Documentation - Create simple setup and usage guides

This workflow provides a structured approach to implementing a focused, reliable MCP server that serves its core purpose effectively without unnecessary complexity.