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

Annotations only provide destructiveHint=true and a title, so the description carries most of the burden. It adds genuinely useful traits beyond the structured data: the cost model ('billed per token processed'), the input prerequisite, and the lifecycle partner (together_cancel_fine_tune). It still omits that the job is asynchronous/long-running and how it is monitored.

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

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

Three short, front-loaded clauses: the action, the cost caveat, the stop path, and the API route. No filler, and the most important information (what it does and what it costs) comes first.

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

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

For a 14-parameter job-creation tool with nested training_type/training_method objects and no output schema, the description is adequate but thin: it doesn't say a job id is returned, that training runs asynchronously, or which sibling tools (get_fine_tune, list_fine_tune_events) track progress.

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

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

Schema description coverage is 79% with 14 parameters, so the baseline is 3. The description adds nothing about model selection, LoRA vs full tuning, epochs, or validation_file beyond what the schema already documents.

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

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

States a specific verb and resource ('Start a fine-tuning job on an uploaded training file') and explicitly names the sibling that stops it, so an agent can distinguish it from together_cancel_fine_tune and together_get_fine_tune without opening schemas.

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

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

It implies the prerequisite (a training file already uploaded) and names the cancel counterpart, but it never states when a fine-tune job is the right operation versus siblings like together_create_batch or together_create_endpoint. Usage is inferable rather than stated.

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