3GPP MCP Server

# 3GPP MCP Server V3.0.0 - Direct Specification Access **Transform your AI assistant into a 3GPP specification expert with direct access to TSpec-LLM's 535M word dataset!** [](https://modelcontextprotocol.io/) [](https://nodejs.org/) [](https://opensource.org/licenses/BSD-3-Clause) ## What This Does **Before**: Ask AI about 3GPP specifications - Get generic responses based on training data **After**: Ask AI + 3GPP MCP Server V3.0.0 - Get direct access to current specification content with structured, agent-ready responses ## Revolutionary V3.0.0 Architecture V3.0.0 represents the **True MCP** approach - lightweight API bridge providing direct specification data: ``` Agent Query → MCP Tools → External APIs → Real Specification Data ``` ### Key Benefits: - **True MCP Architecture** - Lightweight API bridge (~10MB vs 15GB+) - **Sub-500ms responses** - Intelligent caching with external API integration - **Agent-optimized** - Structured JSON responses for AI agent consumption - **Real specification data** - Direct access to TSpec-LLM's 535M word dataset - **External API integration** - Hugging Face + 3GPP.org APIs - **Infinite scalability** - Stateless API calls, no local storage limits ## Quick Start (30 Seconds!) ### Direct MCP Setup (Recommended) **Claude Desktop users:** ```bash claude mcp add 3gpp-server npx 3gpp-mcp-charging@latest serve ``` **For other MCP clients:** Add this to your MCP configuration: ```json { "mcpServers": { "3gpp-server": { "command": "npx", "args": ["3gpp-mcp-charging@latest", "serve"], "description": "3GPP MCP Server - Direct access to TSpec-LLM and 3GPP specifications", "env": { "HUGGINGFACE_TOKEN": "optional-for-enhanced-access" } } } } ``` ### Alternative: Auto-Configuration ```bash # One-command installation with auto-configuration npx 3gpp-mcp-charging@latest init # Client-specific installation npx 3gpp-mcp-charging@latest init --client claude npx 3gpp-mcp-charging@latest init --client vscode npx 3gpp-mcp-charging@latest init --client cursor ``` ### Test It Works Ask your AI assistant: *"Search for 5G CHF implementation requirements in TS 32.290"* You should get structured specification content with implementation guidance, dependencies, and testing considerations! ## Available Tools (V3.0.0) | Tool | Purpose | Input | Output | |------|---------|-------|--------| | `search_specifications` | Direct TSpec-LLM search | Query + filters | Structured spec results + relevance scores | | `get_specification_details` | Comprehensive spec details | Specification ID | Full metadata + implementation guidance | | `compare_specifications` | Multi-spec comparison | Array of spec IDs | Comparison matrix + migration analysis | | `find_implementation_requirements` | Requirements extraction | Spec scope + focus | Technical requirements + testing guidance | ## Example Queries **Direct Specification Search:** ``` "Find charging procedures in 5G service-based architecture" → Returns: TS 32.290 excerpts, CHF implementation details, Nchf interface specifications ``` **Implementation Requirements:** ``` "Extract implementation requirements for converged charging in Release 17" → Returns: Technical requirements, dependencies, testing considerations, compliance notes ``` **Specification Comparison:** ``` "Compare charging evolution from TS 32.240 to TS 32.290" → Returns: Evolution timeline, migration analysis, implementation impact assessment ``` ## What You Get ### **Direct Specification Content** - Real-time access to TSpec-LLM's comprehensive 3GPP dataset - Structured content excerpts with relevance scoring - Official specification metadata integration ### **Agent-Ready Responses** - JSON-formatted responses optimized for AI agent consumption - Consistent schema across all tool responses - Rich metadata embedded in all responses ### **Implementation Intelligence** - Technical requirements extraction from specifications - Dependency analysis and implementation guidance - Testing considerations and compliance mapping ### **Performance Benefits** - <500ms cached response times - <2s fresh API call responses - <10MB memory footprint (stateless design) - Unlimited concurrent users (external API scaling) ## Architecture ### Core Components #### External API Integration Layer - **TSpec-LLM Client**: Direct integration with TSpec-LLM dataset via Hugging Face APIs - **3GPP API Client**: Integration with official 3GPP.org APIs for metadata - **API Manager**: Unified orchestration layer for all external APIs #### MCP Tool Layer - **search_specifications.ts**: Direct specification search implementation - **get_specification_details.ts**: Comprehensive specification details - **compare_specifications.ts**: Multi-specification comparison - **find_implementation_requirements.ts**: Requirements extraction #### Caching & Performance - **NodeCache**: Intelligent API response caching - **Rate Limiting**: Respectful external API usage - **Error Handling**: Robust API integration with fallbacks ## Project Structure ``` 3gpp-mcp-server-v2/ ├── src/ # V3.0.0 source code │ ├── api/ # External API integration layer │ │ ├── tspec-llm-client.ts # TSpec-LLM Hugging Face client │ │ ├── tgpp-api-client.ts # 3GPP.org official API client │ │ ├── api-manager.ts # Unified API orchestration │ │ └── index.ts # API exports │ ├── tools/ # MCP tool implementations │ │ ├── search-specifications.ts # Direct specification search │ │ ├── get-specification-details.ts # Comprehensive spec details │ │ ├── compare-specifications.ts # Multi-spec comparison │ │ ├── find-implementation-requirements.ts # Requirements extraction │ │ └── index.ts # Tool exports │ ├── types/ # TypeScript interfaces │ └── index.ts # MCP server implementation ├── bin/ # CLI installation tools ├── docs/ # Documentation ├── tests/ # Test suite └── package.json # NPM package configuration ``` ## Requirements - **Node.js 18+** - [Download from nodejs.org](https://nodejs.org/) - **MCP-compatible AI assistant** (Claude Desktop, VS Code, Cursor, or others) - **Internet connection** - For external API access - **Optional: Hugging Face token** - For enhanced API access ## Installation Options ### Option 1: Direct MCP Configuration (Recommended) No local installation needed! Server runs directly from NPM. ### Option 2: Development Setup ```bash # Clone and setup for development git clone <repository-url> cd 3gpp-mcp-server/3gpp-mcp-server-v2 npm install npm run build npm run start ``` ### Option 3: Auto-Configuration ```bash npx 3gpp-mcp-charging@latest init ``` ## Environment Variables ```bash # Optional: Enhanced API access export HUGGINGFACE_TOKEN="your-huggingface-token" # Optional: Custom cache settings export CACHE_TIMEOUT="3600" # seconds export ENABLE_CACHING="true" ``` ## Version Evolution | Version | Approach | Storage | Architecture | |---------|----------|---------|-------------| | V1 | Data Hosting | 15GB+ local dataset | Heavy, non-MCP compliant | | V2 | Guidance Templates | <100MB knowledge base | Lightweight, guidance-only | | **V3.0.0** | **Direct Data Access** | **<10MB (stateless)** | **True MCP API bridge** | ## Development ### Available Scripts ```bash npm run build # Build TypeScript npm run dev # Development with watch npm run start # Run the server npm run test # Run tests npm run lint # Lint code npm run clean # Clean build artifacts ``` ### Adding New Tools 1. Create tool class in `src/tools/` 2. Define tool schema with input/output types 3. Implement `execute()` method with API integration 4. Export tool and register in `src/index.ts` ### API Integration - Extend `TSpecLLMClient` for new TSpec-LLM capabilities - Extend `TGPPApiClient` for additional 3GPP.org endpoints - Add orchestration methods to `APIManager` ## Contributing Contributions welcome! Please focus on: - API integration improvements - Performance optimizations - New MCP tool implementations - Documentation enhancements ## License BSD-3-Clause License - see LICENSE file for details. ## Acknowledgments ### Research Foundation This project's V3.0.0 architecture was fundamentally inspired by the TSpec-LLM research: **TSpec-LLM: A Large Language Model for 3GPP Specifications** - Paper: https://arxiv.org/abs/2406.01768 - Authors: Rasoul Nikbakht, et al. - Dataset: [TSpec-LLM on Hugging Face](https://huggingface.co/datasets/rasoul-nikbakht/TSpec-LLM) Originally planned as a document reference MCP, discovery of the TSpec-LLM research paper fundamentally changed our approach. The paper's demonstration of significant accuracy improvements (25+ percentage points) through direct LLM access to 3GPP specifications convinced us to pivot from document hosting to external API integration with their comprehensive 535M word dataset. ### Technical Foundation - Built using the [Model Context Protocol SDK](https://github.com/modelcontextprotocol/sdk) - Integrates with [TSpec-LLM dataset](https://huggingface.co/datasets/rasoul-nikbakht/TSpec-LLM) - Supports 3GPP specifications from [3GPP.org](https://www.3gpp.org/) --- **V3.0.0: True MCP architecture providing direct specification access through external API integration.**