Skip to main content
Glama

groq_chat_completion

Execute fast LLM completions via Groq's API using chat messages or prompts. Supports open models like Llama 3, Mixtral, and Gemma with configurable temperature, max tokens, and stop sequences.

Instructions

Run a fast LLM inference with Groq. Supports Llama 3, Mixtral, Gemma, and other open models at high speed.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyYesGroq API key from console.groq.com/keys
modelNoModel ID (e.g. llama-3.3-70b-versatile, mixtral-8x7b-32768, gemma2-9b-it). Default: llama-3.3-70b-versatile
messagesNoArray of {role, content} messages
promptNoSingle user message (alternative to messages)
system_promptNoSystem prompt (used with prompt shorthand)
max_tokensNoMaximum tokens to generate
temperatureNoSampling temperature (0-2)
top_pNoTop-p sampling (0-1)
stopNoStop sequence(s)
Behavior2/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

No annotations provided, so description carries full burden. Only mentions speed; lacks details on streaming, context length, rate limits, or authorization requirements beyond the schema.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Two concise sentences, front-loaded with action verb and resource. Every word adds value.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness2/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema; description fails to mention return format (e.g., text completion). For a complex tool with 9 parameters, more context (like non-streaming behavior) is needed.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema covers all 9 parameters with descriptions. Description adds no extra meaning beyond listing model names, which the schema already does via default and examples.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

Clear verb+resource: 'Run a fast LLM inference with Groq'. Lists supported models (Llama 3, Mixtral, Gemma), distinguishing it from other LLM tools.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this vs. other chat completion tools (e.g., openai_chat_completion, anthropic_create_message). Mentions speed but no explicit comparison or exclusion criteria.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

Install Server

Other Tools

Latest Blog Posts

MCP directory API

We provide all the information about MCP servers via our MCP API.

curl -X GET 'https://glama.ai/api/mcp/v1/servers/malamutemayhem/unclick'

If you have feedback or need assistance with the MCP directory API, please join our Discord server