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Create Fine-Tuning Job

mistral_create_fine_tuning_job

Start a fine-tuning job by providing a base model and training file IDs. Use it to customize Mistral models for your specific tasks.

Instructions

Create a new fine-tuning job.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
modelYesBase model to fine-tune
suffixNoModel name suffix
dry_runNoValidate only
auto_startNoAuto start
integrationsNoIntegrations
training_filesYesTraining file IDs
hyperparametersNoHyperparameters
validation_filesNoValidation file IDs

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

C2.3/5.0
Behavior2/5

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

Annotations already declare readOnlyHint=false, idempotentHint=false, and destructiveHint=false, so the basic mutation profile is known. The description adds nothing beyond that: it does not say the job is long-running/asynchronous, whether submitting triggers billing or compute, or how dry_run and auto_start affect behavior.

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

Conciseness3/5

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

The single sentence is front-loaded and free of waste, but it is under-specified rather than genuinely concise. There is no structural elaboration to help an agent act on it.

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?

This is a complex, 8-parameter, nested-object creation tool with no output schema and no return-value documentation. At minimum the description should mention the dry-run validation path and the asynchronous nature of fine-tuning jobs, neither of which appears.

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 description coverage is 100% and the schema documents all 8 parameters, including nested integrations and hyperparameters. Per the baseline rule for high coverage, a 3 is appropriate since the schema carries the parameter semantics and the description adds no additional meaning.

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

Purpose3/5

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

The description states a specific verb and resource ('Create a new fine-tuning job'), so an agent can identify the operation. However, it merely restates the tool title with no differentiation from siblings like mistral_start_fine_tuning_job or mistral_get_fine_tuning_job, leaving the create-vs-start distinction to inference.

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

Usage Guidelines1/5

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

There is no guidance on when to use this tool versus alternatives, particularly the closely named mistral_start_fine_tuning_job. No prerequisites (e.g., needing uploaded training files), no mention of dry_run validation workflows, and no exclusions are stated.

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