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AyrtonFelipe

Groq MCP Server

by AyrtonFelipe

groq_text_completion

Generate text completions using AI models with intelligent routing for prompts, supporting parameters like temperature and token limits.

Instructions

Generate text completions using Groq models with intelligent routing

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
promptYes
modelNo
max_tokensNo
temperatureNo
top_pNo
streamNo
json_modeNo
system_promptNo
priorityNo
Behavior2/5

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It mentions 'intelligent routing' which hints at some optimization behavior, but doesn't explain what this entails (e.g., automatic model selection, performance tuning, cost optimization). It fails to disclose critical behavioral traits like rate limits, authentication requirements, error handling, or what 'completions' specifically means in terms of output format or length.

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

Conciseness4/5

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

The description is appropriately concise at just one sentence with no wasted words. It's front-loaded with the core purpose ('Generate text completions') and efficiently adds the service provider and a key feature. However, the 'intelligent routing' phrase adds some ambiguity that slightly reduces clarity.

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?

Given the complexity of a 9-parameter tool with no annotations and no output schema, the description is insufficiently complete. It doesn't explain what the tool returns, how 'intelligent routing' works, what models are available, or provide any parameter guidance. For a text generation API with multiple configuration options, this leaves too many gaps for effective agent use.

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

Parameters2/5

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

With 0% schema description coverage and 9 parameters, the description provides no information about any parameters. It doesn't explain what 'prompt', 'model', 'temperature', 'max_tokens', or other parameters mean or how they affect the completion. The description fails to compensate for the complete lack of schema documentation, leaving all parameters semantically undefined.

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

Purpose4/5

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

The description clearly states the action ('Generate text completions') and the resource/service ('using Groq models'), which provides a specific verb+resource combination. However, it doesn't explicitly differentiate from sibling tools like groq_audio_transcription or groq_vision_analysis beyond the 'text' focus, missing explicit sibling differentiation that would warrant a 5.

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?

The description provides no guidance on when to use this tool versus alternatives. It mentions 'intelligent routing' but doesn't explain what this means in practice or when to choose this over other text generation tools. There are no explicit when/when-not statements or references to sibling tools, resulting in minimal usage guidance.

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

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