Experimental Model Context Protocol server enabling AI models to measure and analyze internet speed, latency, jitter, and bandwidth with detailed JSON reporting for enhanced network performance evaluation.
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This is an experimental implementation of a Model Context Protocol (MCP) server for internet speed testing. It allows AI models and agents to measure, analyze, and report network performance metrics through a standardized interface.
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.
The uvx
command is a convenient way to run Python packages directly without explicit installation:
# Run the MCP server directly uvx /path/to/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
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": "uv", "args": [ "--directory", "/ABSOLUTE/PATH/TO/mcp-internet-speed-test", "run", "main.py" ] } } }
The MCP Internet Speed Test provides the following tools:
measure_download_speed
: Measures download bandwidth (in Mbps)measure_upload_speed
: Measures upload bandwidth (in Mbps)measure_latency
: Measures network latency (in ms)measure_jitter
: Measures network jitter by analyzing latency variationsrun_complete_test
: Runs all tests and provides a comprehensive reportIf you're having issues connecting to the MCP server:
mcp[cli]
and httpx
packages are installedThis is an experimental project and contributions are welcome. To contribute:
This project is licensed under the MIT License - see the LICENSE file for details.
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