3gpp-mcp-server/docs/technical-approach.md

MCP Server for querying 3GPP telecom protocol specifications

# Technical Approach & Architecture

## Overview

This document outlines the technical approach, architecture decisions, and implementation strategy for the 3GPP MCP Server. Our approach combines semantic search, domain-specific knowledge modeling, and the Model Context Protocol to create an intelligent interface for 3GPP telecommunications specifications.

## Architecture Overview

### High-Level System Architecture

```mermaid
graph TB
    subgraph "Client Layer"
        A[Claude Desktop] --> B[Other MCP Clients]
        B --> C[Custom Applications]
    end

    subgraph "MCP Protocol Layer"
        D[MCP Server Interface]
        D1[Tools Handler] --> D
        D2[Resources Handler] --> D
        D3[Prompts Handler] --> D
    end

    subgraph "Intelligence Layer"
        E[Specification Manager]
        F[Search Engine]
        G[Document Processor]
        H[RAG Engine]
    end

    subgraph "Data Layer"
        I[TSpec-LLM Dataset]
        J[Vector Database]
        K[Metadata Cache]
        L[Configuration Store]
    end

    A --> D
    D1 --> E
    D1 --> F
    D2 --> E
    D3 --> G

    E --> I
    F --> J
    G --> I
    H --> J

    E --> K
    F --> K
```

## Core Components

### 1. MCP Server Interface (`src/index.ts`)

**Responsibility**: Implements the Model Context Protocol specification

**Key Features**:
- **Tools Handlers**: Process semantic search, specification lookup, protocol queries
- **Resources Handlers**: Provide access to catalogs, timelines, protocol lists
- **Prompts Handlers**: Generate structured prompts for complex queries
- **Error Handling**: Robust error management and user feedback

**Implementation Details**:
```typescript
class GPPMCPServer {
  - server: MCP Server instance
  - specManager: Manages specification metadata
  - searchEngine: Handles intelligent search
  - documentProcessor: Processes specification content
}
```

### 2. Specification Manager (`src/utils/specification-manager.ts`)

**Responsibility**: Manages the 3GPP specification collection and metadata

**Core Capabilities**:
- **Specification Loading**: Discovers and loads specifications from TSpec-LLM dataset
- **Metadata Extraction**: Extracts titles, series, releases, keywords from documents
- **Catalog Management**: Maintains searchable catalog of all specifications
- **Protocol Mapping**: Maps protocols to their defining specifications

**Data Structures**:
```typescript
interface GPPSpecification {
  id: string              // e.g., "TS 24.301"
  title: string           // Human-readable title
  series: string          // Specification series (24, 25, 36, 38, etc.)
  release: string         // 3GPP release (Rel-15, Rel-16, etc.)
  version: string         // Specification version
  filePath: string        // Path to source document
  format: 'markdown' | 'docx'
  size: number           // File size in bytes
  lastModified: Date
  metadata?: GPPSpecMetadata
}
```

### 3. Search Engine (`src/utils/search-engine.ts`)

**Responsibility**: Provides intelligent search across 3GPP specifications

**Search Algorithms**:
- **Semantic Search**: Content-based relevance scoring
- **Metadata Filtering**: Filter by series, release, keywords
- **Cross-Reference Resolution**: Find related specifications
- **Contextual Ranking**: Domain-aware result ranking

**Search Process**:
1. **Query Analysis**: Parse natural language query
2. **Filtering**: Apply series/release constraints
3. **Semantic Matching**: Score documents against query
4. **Result Ranking**: Sort by relevance and domain context
5. **Context Generation**: Extract relevant snippets

### 4. Document Processor (`src/utils/document-processor.ts`)

**Responsibility**: Extracts and processes content from specification documents

**Processing Pipeline**:
- **Content Extraction**: Parse markdown and DOCX formats
- **Metadata Parsing**: Extract technical metadata from documents
- **Section Analysis**: Identify document structure and key sections
- **Keyword Extraction**: Identify 3GPP-specific terminology
- **Summary Generation**: Create structured summaries

**Supported Formats**:
- **Markdown (.md)**: Primary format from TSpec-LLM dataset
- **DOCX (.docx)**: Microsoft Word format support (via mammoth.js)
- **Future**: PDF support for legacy specifications

## Data Architecture

### TSpec-LLM Dataset Integration

**Dataset Characteristics**:
- **Size**: 13.5 GB, 30,137 documents, 535M words
- **Coverage**: 3GPP Releases 8-19 (1999-2023)
- **Format**: Processed markdown with preserved structure
- **Quality**: Full content preservation including tables, formulas

**File Organization**:
```
TSpec-LLM/
├── Rel-8/
│   ├── series-21/
│   ├── series-22/
│   └── ...
├── Rel-9/
├── ...
└── Rel-19/
```

**Loading Strategy**:
1. **Lazy Loading**: Load specifications on-demand
2. **Metadata Caching**: Cache frequently accessed metadata
3. **Incremental Indexing**: Build search index progressively
4. **Memory Management**: Efficient handling of large dataset

### Data Model Design

**Specification Metadata Model**:
```typescript
interface GPPSpecMetadata {
  workingGroup?: string       // Responsible working group
  technicalArea?: string      // Technical domain
  keywords?: string[]         // 3GPP-specific terms
  abstract?: string          // Document abstract
  stage?: 1 | 2 | 3         // 3GPP development stage
  dependencies?: string[]     // Referenced specifications
}
```

**Protocol Model**:
```typescript
interface GPPProtocol {
  name: string               // Short name (NAS, RRC)
  fullName: string          // Full protocol name
  series: string            // Primary specification series
  specifications: string[]   // Defining specifications
  procedures?: ProtocolProcedure[]
}
```

## Search & Retrieval Strategy

### Multi-Level Search Architecture

**Level 1: Metadata Search**
- Fast filtering by series, release, specification ID
- Keyword matching in titles and abstracts
- Protocol and procedure name matching

**Level 2: Content Search**
- Full-text search within document content
- Section-aware search (scope, procedures, definitions)
- Technical term recognition and expansion

**Level 3: Semantic Search** (Future Enhancement)
- Vector embeddings for semantic similarity
- Cross-document concept matching
- Contextual relevance scoring

### Relevance Scoring Algorithm

**Multi-Factor Scoring**:
```typescript
relevanceScore = (
  0.4 * titleMatch +        // Title keyword matches
  0.3 * keywordMatch +      // Metadata keyword matches
  0.2 * abstractMatch +     // Abstract content matches
  0.3 * specIdMatch +       // Direct specification references
  0.2 * contextBoost       // Domain context boost
)
```

**Domain Context Factors**:
- Protocol relationships (NAS ↔ Authentication)
- Release evolution (Rel-15 → Rel-16 changes)
- Technical dependencies (RRC → PDCP → RLC)

## Query Processing Pipeline

### Natural Language Query Processing

**Pipeline Stages**:
1. **Query Parsing**: Extract key terms and intent
2. **Domain Mapping**: Map terms to 3GPP concepts
3. **Query Expansion**: Add related terms and synonyms
4. **Filter Application**: Apply series/release constraints
5. **Search Execution**: Execute multi-level search
6. **Result Synthesis**: Combine and rank results
7. **Response Generation**: Format for user consumption

**Example Query Flow**:
```
Input: "How does 5G NAS authentication work?"

1. Parse: ["5G", "NAS", "authentication", "work"]
2. Map: 5G → Rel-15+, NAS → series-24, authentication → security procedures
3. Expand: [authentication, security, SUCI, SUPI, AUSF, UDM]
4. Filter: series=24, release≥Rel-15
5. Search: Find TS 24.501, related security specs
6. Synthesize: Combine procedure information
7. Generate: Structured response with steps and references
```

### Response Generation Strategy

**Structured Response Format**:
```typescript
interface QueryResponse {
  summary: string           // Brief answer overview
  details: string[]         // Detailed explanations
  procedures?: string[]     // Step-by-step procedures
  specifications: string[]  // Referenced specifications
  relatedTopics: string[]   // Suggested related queries
  messageFlows?: MessageFlow[] // Protocol message flows
}
```

## Performance Considerations

### Optimization Strategies

**Memory Management**:
- Lazy loading of specification content
- LRU cache for frequently accessed documents
- Memory-mapped file access for large documents

**Query Performance**:
- Metadata index for fast filtering
- Bloom filters for existence checks
- Result caching with TTL expiration

**Scalability Design**:
- Horizontal scaling support for search operations
- Stateless server design for multiple instances
- Database connection pooling

### Performance Targets

**Response Time Goals**:
- Simple queries (spec lookup): < 500ms
- Complex searches: < 3 seconds
- Cross-specification analysis: < 5 seconds

**Throughput Goals**:
- 100+ concurrent queries
- 1000+ specifications loaded
- Memory usage < 2GB per instance

## Integration Architecture

### MCP Protocol Implementation

**Tool Interface**:
```typescript
interface MCPTool {
  name: string
  description: string
  inputSchema: JSONSchema
  handler: (params: any) => Promise<MCPResponse>
}
```

**Resource Interface**:
```typescript
interface MCPResource {
  uri: string
  name: string
  description: string
  mimeType: string
  generator: () => Promise<string>
}
```

**Prompt Interface**:
```typescript
interface MCPPrompt {
  name: string
  description: string
  arguments: PromptArgument[]
  generator: (args: any) => Promise<PromptMessage[]>
}
```

### Client Integration Patterns

**Claude Desktop Integration**:
```json
{
  "mcpServers": {
    "3gpp-server": {
      "command": "node",
      "args": ["dist/index.js"],
      "cwd": "/path/to/3gpp-mcp-server"
    }
  }
}
```

**Custom Client Integration**:
```typescript
const client = new Client({
  name: "3gpp-client",
  version: "1.0.0"
}, {
  capabilities: {}
});

await client.connect(transport);
const tools = await client.listTools();
```

## Technology Stack

### Core Technologies

**Runtime Environment**:
- **Node.js 18+**: JavaScript runtime
- **TypeScript**: Type safety and developer experience
- **ES2022**: Modern JavaScript features

**MCP Implementation**:
- **@modelcontextprotocol/sdk**: Official MCP SDK
- **Zod**: Schema validation and type inference

**Document Processing**:
- **fs-extra**: Enhanced file system operations
- **mammoth.js**: DOCX to text conversion (future)
- **markdown-it**: Markdown parsing (future enhancement)

**Future Enhancements**:
- **ChromaDB**: Vector database for semantic search
- **OpenAI Embeddings**: Semantic similarity computation
- **Redis**: Caching and session management

### Development Tools

**Build & Development**:
- **TypeScript Compiler**: Source transformation
- **ESLint**: Code quality and consistency
- **Jest**: Unit testing framework
- **ts-node**: Development runtime

**Documentation**:
- **Markdown**: Documentation format
- **Mermaid**: Diagram generation
- **TypeDoc**: API documentation generation

## Security & Privacy

### Security Considerations

**Data Protection**:
- Read-only access to specification documents
- No modification of source data
- Secure file path handling

**Network Security**:
- STDIO transport (no network exposure by default)
- Optional HTTPS transport for remote access
- Input validation and sanitization

**Access Control**:
- MCP client authentication (when applicable)
- Rate limiting for query operations
- Resource usage monitoring

### Privacy Considerations

**Data Handling**:
- No user query logging by default
- Anonymized usage statistics only
- No personally identifiable information storage

**Compliance**:
- 3GPP specification licensing compliance
- Open source dataset usage (TSpec-LLM)
- MIT license for server implementation

## Deployment Architecture

### Development Deployment
- Local development environment
- Direct STDIO connection
- Hot reload during development

### Production Deployment
- Containerized deployment (Docker)
- Process management (PM2)
- Load balancing for multiple instances
- Monitoring and logging

### Cloud Deployment Options
- **Container Services**: AWS ECS, Azure Container Instances
- **Serverless**: AWS Lambda, Azure Functions (with modifications)
- **Kubernetes**: Full orchestration for enterprise deployment

This technical approach provides a robust, scalable foundation for intelligent 3GPP specification access while maintaining the flexibility to evolve and add new capabilities as requirements grow.