MCP Internet Speed Test

The MCP Internet Speed Test server equips AI assistants with a robust, standardized interface for measuring network performance. By exposing a rich set of capabilities—download and upload throughput, latency, jitter, CDN identification, and cache behavior—the tool transforms raw network diagnostics into consumable data that models can ingest, analyze, and report on. This enables developers to build context-aware applications where an AI agent can, for example, recommend optimal server locations or troubleshoot connectivity issues without manual intervention.

At its core, the server implements a Smart Incremental Testing strategy inspired by SpeedOf.Me. It progressively probes bandwidth using files ranging from 128 KB to 100 MB, ensuring accurate results while keeping test duration short (≈8 seconds). Upload tests generate payloads of similar sizes, allowing the assistant to gauge both inbound and outbound capacities. Latency is measured against a suite of geographically diverse CDN endpoints (Fastly, Cloudflare, AWS CloudFront), providing not only round‑trip times but also detailed server location data. Jitter is derived from multiple latency samples, giving insight into network stability—critical for real‑time applications such as video conferencing or online gaming.

The server’s multi‑CDN support and POP‑code mapping mean that an AI can identify the exact cache node serving a request, detect HIT/MISS status, and read CDN‑specific headers (, , ). This level of granularity is invaluable for content‑delivery optimization, troubleshooting CDN misconfigurations, or validating that a global distribution strategy is working as intended. Developers can leverage the single “run all tests” endpoint to obtain a comprehensive report in one go, streamlining workflows that previously required multiple tools.

Integrating this MCP into an AI workflow is straightforward: the assistant simply calls the server’s resources, receives structured JSON metrics, and can embed them in explanations, dashboards, or automated alerts. For example, a chatbot could answer “What’s my current upload speed?” by invoking the server and returning a concise, user‑friendly summary. In more complex scenarios, an agent could monitor network health over time, trigger remediation steps when latency spikes, or guide users to the nearest optimal CDN node.

Unique advantages of this MCP include its adherence to a proven testing methodology, real‑time multi‑CDN analysis, and full compatibility with any MCP‑compliant client. By abstracting the intricacies of network measurement behind a simple, declarative protocol, it empowers developers to build smarter, more responsive AI assistants that can diagnose and adapt to the ever‑changing landscape of internet connectivity.