Groq MCP

Groq runs open weight language, speech, and vision models on its own inference hardware, which is built for very high token throughput and low time to first token. The Groq MCP server puts that API behind Model Context Protocol tools, so an assistant that already speaks MCP can send work to Groq without any custom HTTP client code in between. The server exposes Groq's main endpoints as callable tools: chat completion against the models hosted on the platform, speech to text for transcribing and translating audio files, text to speech for turning a passage into an audio file on disk, and image analysis through vision capable models. Because the tools accept file paths as well as text, an agent can chain them, transcribing a recording, summarizing it with a fast model, then reading the summary back as audio. The practical use is offloading. A capable but expensive reasoning model stays in charge of the conversation while bulk work such as transcription, classification, or first draft generation goes to Groq and comes back quickly. It suits voice interfaces, meeting and podcast pipelines, and any agent loop where latency per step compounds. You supply your own Groq API key, and usage is billed by the platform rather than by the server itself.