An implementation of a Model Context Protocol (MCP) for internet speed testing. It allows AI models and agents to measure, analyze, and report network performance metrics through a standardized interface.

📦 Available on PyPI: https://pypi.org/project/mcp-internet-speed-test/

🚀 Quick Start:

pip install mcp-internet-speed-test
mcp-internet-speed-test

The Model Context Protocol (MCP) provides a standardized way for Large Language Models (LLMs) to interact with external tools and data sources. Think of it as the "USB-C for AI applications" - a common interface that allows AI systems to access real-world capabilities and information.

• Smart Incremental Testing: Uses SpeedOf.Me methodology with 8-second threshold for optimal accuracy
• Download Speed Testing: Measures bandwidth using files from 128KB to 100MB from GitHub repository
• Upload Speed Testing: Tests upload bandwidth using generated data from 128KB to 100MB
• Latency Testing: Measures network latency with detailed server location information
• Jitter Analysis: Calculates network stability using multiple latency samples (default: 5)
• Multi-CDN Support: Detects and provides info for Fastly, Cloudflare, and AWS CloudFront
• Geographic Location: Maps POP codes to physical locations (50+ locations worldwide)
• Cache Analysis: Detects HIT/MISS status and cache headers
• Server Metadata: Extracts detailed CDN headers including x-served-by, via, x-cache
• Comprehensive Testing: Single function to run all tests with complete metrics

• Python 3.12 or higher (required for async support)
• pip or uv package manager

# Install the package globally
pip install mcp-internet-speed-test
# Run the MCP server
mcp-internet-speed-test

# Install the package globally
uv add mcp-internet-speed-test
# Or run directly without installing
uvx mcp-internet-speed-test

# Build the Docker image
docker build -t mcp-internet-speed-test .
# Run the MCP server in a Docker container
docker run -it --rm -v $(pwd):/app -w /app mcp-internet-speed-test

If you want to contribute or modify the code:

# Clone the repository
git clone https://github.com/inventer-dev/mcp-internet-speed-test.git
cd mcp-internet-speed-test
# Install in development mode
pip install -e .
# Or using uv
uv sync
uv run python -m mcp_internet_speed_test.main

The package automatically installs these dependencies:

• mcp[cli]>=1.6.0: MCP server framework with CLI integration
• httpx>=0.27.0: Async HTTP client for speed tests

To use this MCP server with Claude Desktop or other MCP clients, add it to your MCP configuration file.

Edit your Claude Desktop MCP configuration file:

{
    "mcpServers": {
        "mcp-internet-speed-test": {
            "command": "mcp-internet-speed-test"
        }
    }
}

{
    "mcpServers": {
        "mcp-internet-speed-test": {
            "command": "uvx",
            "args": ["mcp-internet-speed-test"]
        }
    }
}

The MCP Internet Speed Test provides the following tools:

1. measure_download_speed: Measures download bandwidth (in Mbps) with server location info
2. measure_upload_speed: Measures upload bandwidth (in Mbps) with server location info
3. measure_latency: Measures network latency (in ms) with server location info
4. measure_jitter: Measures network jitter by analyzing latency variations with server info
5. get_server_info: Get detailed CDN server information for any URL without running speed tests
6. run_complete_test: Comprehensive test with all metrics and server metadata

This speed test now provides detailed information about the CDN servers serving your tests:

• CDN Provider: Identifies if you're connecting to Fastly, Cloudflare, or Amazon CloudFront
• Geographic Location: Shows the physical location of the server (e.g., "Mexico City, Mexico")
• POP Code: Three-letter code identifying the Point of Presence (e.g., "MEX", "QRO", "DFW")
• Cache Status: Whether content is served from cache (HIT) or fetched from origin (MISS)
• Server Headers: Full HTTP headers including x-served-by, via, and x-cache

• Incremental Approach: Starts with small files (128KB) and progressively increases
• Time-Based Optimization: Uses 8-second base threshold + 4-second additional buffer
• Accuracy Focus: Selects optimal file size that provides reliable measurements
• Multi-Provider Support: Tests against geographically distributed endpoints

• Fastly: Detects POP codes and maps to 50+ global locations
• Cloudflare: Identifies data centers and geographic regions
• AWS CloudFront: Recognizes edge locations across continents
• Header Analysis: Parses x-served-by, via, x-cache, and custom CDN headers

• Network Diagnostics: Understand which server is actually serving your tests
• Performance Analysis: Correlate speed results with server proximity
• CDN Optimization: Identify if your ISP's routing is optimal
• Geographic Awareness: Know if tests are running from your expected region
• Troubleshooting: Identify routing issues and CDN misconfigurations

{
  "cdn_provider": "Fastly",
  "pop_code": "MEX",
  "pop_location": "Mexico City, Mexico",
  "served_by": "cache-mex4329-MEX",
  "cache_status": "HIT",
  "x_cache": "HIT, HIT"
}

GitHub Repository: inventer-dev/speed-test-files
Branch: main
File Sizes: 128KB, 256KB, 512KB, 1MB, 2MB, 5MB, 10MB, 20MB, 40MB, 50MB, 100MB

1. Cloudflare Workers (httpi.dev) - Global distribution, highest priority
2. HTTPBin (httpbin.org) - AWS-based, secondary endpoint

Fastly POPs: MEX, QRO, DFW, LAX, NYC, MIA, LHR, FRA, AMS, CDG, NRT, SIN, SYD, GRU, SCL, BOG, MAD, MIL...

Cloudflare Centers: DFW, LAX, SJC, SEA, ORD, MCI, IAD, ATL, MIA, YYZ, LHR, FRA, AMS, CDG, ARN, STO...

AWS CloudFront: ATL, BOS, ORD, CMH, DFW, DEN, IAD, LAX, MIA, MSP, JFK, SEA, SJC, AMS, ATH, TXL...

• Base Test Duration: 8.0 seconds
• Additional Buffer: 4.0 seconds
• Maximum File Size: Configurable (default: 100MB)
• Jitter Samples: 5 measurements (configurable)

1. Path Configuration: Ensure absolute path is used in MCP configuration
2. Directory Permissions: Verify read/execute permissions for the project directory
3. Python Version: Requires Python 3.12+ with async support
4. Dependencies: Install fastmcp and httpx packages

1. GitHub Repository Access: Ensure inventer-dev/speed-test-files is accessible
2. Firewall/Proxy: Check if corporate firewalls block test endpoints
3. CDN Routing: Some ISPs may route differently to CDNs
4. Network Stability: Jitter tests require stable connections

• File Size Limits: Large files (>50MB) may timeout on slow connections
• Upload Endpoints: If primary endpoint fails, fallback is automatic
• Geographic Accuracy: POP detection depends on CDN header consistency

mcp-internet-speed-test/
├── mcp_internet_speed_test/  # Main package directory
│   ├── __init__.py      # Package initialization
│   └── main.py          # MCP server implementation
├── README.md           # This documentation
├── Dockerfile          # Container configuration
└── pyproject.toml      # Python project configuration

• GITHUB_RAW_URL: Base URL for test files repository
• UPLOAD_ENDPOINTS: Prioritized list of upload test endpoints
• SIZE_PROGRESSION: Ordered list of file sizes for incremental testing
• *_POP_LOCATIONS: Mappings of CDN codes to geographic locations

• extract_server_info(): Parses HTTP headers to identify CDN providers
• measure_*(): Individual test functions for different metrics
• run_complete_test(): Orchestrates comprehensive testing suite

You can customize the following in mcp_internet_speed_test/main.py if you clone the repository:

# GitHub repository settings
GITHUB_USERNAME = "your-username"
GITHUB_REPO = "your-speed-test-files"
GITHUB_BRANCH = "main"
# Test duration thresholds
BASE_TEST_DURATION = 8.0  # seconds
ADDITIONAL_TEST_DURATION = 4.0  # seconds
# Default endpoints
DEFAULT_UPLOAD_URL = "your-upload-endpoint"
DEFAULT_LATENCY_URL = "your-latency-endpoint"

This is an experimental project and contributions are welcome:

1. Issues: Report bugs or request features
2. Pull Requests: Submit code improvements
3. Documentation: Help improve this README
4. Testing: Test with different network conditions and CDNs

This project is licensed under the MIT License - see the LICENSE file for details.

• MCP Framework maintainers for standardizing AI tool interactions
• The Model Context Protocol community for documentation and examples