get fine tuning job

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

Annotations already mark this read-only, idempotent, and non-destructive. The description adds meaningful behavioral detail that annotations cannot convey: live progress is fetched best-effort from the training backend and falls back to DB state, a SUCCEEDED job remains not-servable until deployed, and scopes are not enforced on this route. Nothing contradicts the annotations; instead it reinforces the read-only nature with 'Read-only, no spend'.

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 every sentence earns its place: purpose, endpoint, auth scope, full return contract, error conditions, backend fallback, deployment nuance, feature flag, and safety notes are all essential for correct invocation. The front-loaded sentence states the core purpose immediately, followed by structured details. No redundant filler exists.

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 output schema, the description must disclose return fields, which it does exhaustively, including null states and enums. It also covers auth, errors, feature flags, live-data behavior, and deployment semantics, making it complete for an agent to call the tool and interpret the result correctly. Given the tool's complexity, nothing critical is missing.

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 100% and the single parameter `id` is already well described in the schema ('The job id returned by POST /v1/fine_tuning/jobs, not the provider_job_id'). The description does not need to add parameter semantics, so it sits at baseline 3. It does indirectly clarify id usage by returning `provider_job_id` in the response, but that is not directly about the input parameter.

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 opens with a specific verb and resource: 'Get one fine-tuning job's live status, step progress, trained tokens, output model name, deployment state and billed cost'. It enumerates exactly what is returned and distinguishes itself from list/cance/other fine-tuning tools by targeting a single job. It also provides the exact endpoint, leaving no ambiguity about the operation.

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?

Explicit guidance is given: 'poll this after creating a job', which tells an agent when this tool is appropriate. It also communicates contextual constraints like 404 when the job is not in the workspace and feature-flag gating. It does not explicitly contrast with alternatives such as list_fine_tuning_jobs or cancel_fine_tuning_job, though the one-job scope implies the distinction.

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