Groq MCP Server
Server Configuration
Describes the environment variables required to run the server.
| Name | Required | Description | Default |
|---|---|---|---|
| LOG_LEVEL | No | Optional: Configure the logging level | |
| GROQ_API_KEY | Yes | Your API key to authenticate requests to the Groq API. Obtain it at https://console.groq.com/keys |
Instructions
Guidance the server publishes about itself, which clients place ahead of the tool catalog so the model reads it before choosing anything.
This server publishes no instructions, or was last inspected before Glama recorded them.
Capabilities
Features and capabilities supported by this server
Protocol revision2025-11-25
| Capability | Details |
|---|---|
| tools | {} |
| logging | {} |
| prompts | {} |
| resources | {} |
Tools
Functions exposed to the LLM to take actions
| Name | Description |
|---|---|
| groq_text_completionC | Generate text completions using Groq models with intelligent routing |
| groq_audio_transcriptionC | Transcribe audio files using Groq Whisper models |
| groq_vision_analysisC | Analyze images using Groq multimodal models |
| groq_batch_processingC | Process large batches of requests with 25% discount |
Prompts
Interactive templates invoked by user choice
| Name | Description |
|---|---|
No prompts | |
Resources
Contextual data attached and managed by the client
| Name | Description |
|---|---|
No resources | |
TDQS
Scored across 4 tools
Each tool has a clearly distinct purpose targeting different Groq service capabilities: audio transcription, batch processing, text completion, and vision analysis. There is no overlap or ambiguity in their functions, making it easy for an agent to select the appropriate tool.
All tool names follow a consistent 'groq_' prefix with descriptive snake_case suffixes (e.g., groq_audio_transcription, groq_batch_processing). This uniform pattern enhances predictability and readability across the tool set.
With 4 tools, the count is reasonable for a Groq API server, covering core functionalities like text, audio, vision, and batch processing. It is slightly lean but well-scoped, as each tool serves a distinct purpose without redundancy.
The tool set covers key Groq model types (text, audio, vision) and includes batch processing for efficiency, which addresses common use cases. A minor gap might be the lack of specific tools for model management or configuration, but the surface is largely complete for typical inference tasks.