list training files

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

The description goes well beyond the annotations by disclosing the exact response shape (bare JSON array, no envelope), the field semantics (especially provider_file_id being the opaque upstream id to use), error shape for feature-flag gating (plain string error not nested), and the lack of spend. This is useful operational detail that annotations alone do not convey.

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?

The description is long but packed with useful information: endpoint, scope, return format, field meanings, error behavior, auth nuance, and cost. It is front-loaded with the core purpose and ordering, then layers supporting details. Slightly dense, but every clause earns its place given the absence of an output schema.

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?

With no parameters, no output schema, and rich annotations, the description covers everything an agent needs: what is returned, which field to use downstream, what errors to expect, auth behavior, and side-effect/cost implications. No material gap remains for correct invocation and interpretation.

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?

There are zero parameters and the schema coverage is 100%, so the description has no parameter burden. It instead clarifies what the returned fields mean, especially provider_file_id, which is the kind of semantic detail that would otherwise be missing. A 4 reflects the baseline for a no-parameter tool plus the added value of explaining result semantics.

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?

The description identifies a specific verb and resource: 'List the training files this workspace has uploaded for fine-tuning, newest first'. It also clarifies the purpose—finding the file id to start a job—and distinguishes itself from related operations by naming the exact endpoint (GET /v1/fine_tuning/files) and emphasizing the provider_file_id as the value to pass to job creation.

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 explicitly states when to use this tool: when a customer needs to find a file id to start a fine-tuning job. It also provides critical context about feature-flag gating (404 when fineTuning is off) and authentication behavior (any valid workspace key works; management scopes not enforced), which helps the agent decide whether this tool is accessible in the current context.

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