3GPP MCP Server
Integrates with Hugging Face APIs to access the TSpec-LLM dataset containing 535M words of 3GPP specification data for direct specification search and analysis
Click on "Deploy Server".
Wait a few minutes for the server to deploy. Once ready, it will show a "Started" state.
In the chat, type
@followed by the MCP server name and your instructions, e.g., "@3GPP MCP Server search for 5G charging function implementation requirements in TS 32.290"
That's it! The server will respond to your query, and you can continue using it as needed.
Here is a step-by-step guide with screenshots.
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!
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
Related MCP server: IEEE 802.11 MCP Server
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 DataKey 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:
claude mcp add 3gpp-server npx 3gpp-mcp-charging@latest serveFor other MCP clients: Add this to your MCP configuration:
{
"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
# 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 cursorTest 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 |
| Direct TSpec-LLM search | Query + filters | Structured spec results + relevance scores |
| Comprehensive spec details | Specification ID | Full metadata + implementation guidance |
| Multi-spec comparison | Array of spec IDs | Comparison matrix + migration analysis |
| 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 specificationsImplementation Requirements:
"Extract implementation requirements for converged charging in Release 17"
→ Returns: Technical requirements, dependencies, testing considerations, compliance notesSpecification Comparison:
"Compare charging evolution from TS 32.240 to TS 32.290"
→ Returns: Evolution timeline, migration analysis, implementation impact assessmentWhat 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 configurationRequirements
Node.js 18+ - Download from 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
# Clone and setup for development
git clone <repository-url>
cd 3gpp-mcp-server/3gpp-mcp-server-v2
npm install
npm run build
npm run startOption 3: Auto-Configuration
npx 3gpp-mcp-charging@latest initEnvironment Variables
# 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
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 artifactsAdding New Tools
Create tool class in
src/tools/Define tool schema with input/output types
Implement
execute()method with API integrationExport tool and register in
src/index.ts
API Integration
Extend
TSpecLLMClientfor new TSpec-LLM capabilitiesExtend
TGPPApiClientfor additional 3GPP.org endpointsAdd 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
Authors: Rasoul Nikbakht, et al.
Dataset: TSpec-LLM on Hugging Face
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
Integrates with TSpec-LLM dataset
Supports 3GPP specifications from 3GPP.org
V3.0.0: True MCP architecture providing direct specification access through external API integration.
Available Tools
4 toolscompare_specificationsC
Compare multiple 3GPP specifications across various criteria including architecture, procedures, evolution, and implementation differences.
Inputs: specification_ids (required), comparison_criteria, include_evolution_analysis, format.
find_implementation_requirementsC
Extract detailed implementation requirements for specific 3GPP features, including mandatory/optional requirements, dependencies, and implementation guidance.
Inputs: feature (required), domain, complexity_level, include_dependencies, +2 more.
get_specification_detailsB
Get comprehensive details about a specific 3GPP specification including metadata, content, dependencies, and related information.
Inputs: specification_id (required), include_content, include_dependencies, format.
search_specificationsA
Search 3GPP specifications using TSpec-LLM dataset and official metadata. Returns actual specification content and structured metadata for agent consumption.
Inputs: query (required), max_results, series_filter, release_filter, +2 more.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections.
Loading tool schema changelog…
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose: compare_specifications focuses on cross-specification analysis, find_implementation_requirements extracts feature-specific requirements, get_specification_details provides metadata and content for a single spec, and search_specifications enables discovery across the dataset. There is no overlap in functionality, making tool selection unambiguous for an agent.
All tool names follow a consistent verb_noun pattern with snake_case formatting: compare_specifications, find_implementation_requirements, get_specification_details, and search_specifications. The verbs (compare, find, get, search) are distinct and appropriate for their actions, creating a predictable and readable naming convention throughout the set.
With 4 tools, the count is reasonable for a 3GPP specification server, covering core operations like search, retrieval, comparison, and requirement extraction. It is slightly lean but well-scoped; additional tools for updates or management might be expected in a broader system, but this set effectively supports key agent workflows without bloat.
The tool set provides strong coverage for querying and analyzing 3GPP specifications, including search, detailed retrieval, comparison, and implementation requirements. Minor gaps exist, such as lack of tools for modifying or managing specifications (e.g., create, update, delete), but these may be outside the server's read-only scope, and agents can perform core analysis tasks without dead ends.
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