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mistral_chat_completion

Generate chat responses using Mistral AI models. Supply a user prompt or message history, and configure model, temperature, and token limits for customized output.

Instructions

Run a chat completion with a Mistral AI model (mistral-small, mistral-medium, mistral-large, etc.).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
api_keyNoMistral API key
modelNoModel ID (default: mistral-small-latest)
promptNoSingle user message (alternative to messages)
system_promptNo
messagesNoJSON array of {role, content} messages
max_tokensNo
temperatureNo
top_pNo
Behavior2/5

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

With no annotations, the description carries full responsibility. It discloses the core action but omits critical behavioral details such as token limits, streaming support, error handling, cost implications, or authentication requirements (api_key is a parameter but not mentioned). The description provides minimal behavioral insight.

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 a single succinct sentence, front-loading the key verb and resource. It is efficient with no wasted words, though it could be slightly more structured to improve scannability.

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 tool has 8 parameters and no output schema, the description is overly brief. It does not mention required parameters (prompt or messages), return format, or potential errors, leaving agents without sufficient context for correct invocation.

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?

The description adds no parameter information beyond what the input schema already provides. Schema coverage is 50% (4 of 8 parameters described), and the description does not compensate for the undocumented parameters, such as system_prompt, max_tokens, temperature, and top_p.

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 tool performs a chat completion using Mistral AI models, listing example model IDs. This distinguishes it from non-chat tools, but does not explicitly differentiate from other chat completion tools among siblings, though the mention of 'Mistral' implies the provider.

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 is given on when to use this tool instead of alternatives (e.g., OpenAI, Groq, etc.), nor any prerequisites or explicit exclusions. The description simply states what it does without context for selection.

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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