train_serve

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

With no annotations, the description carries the transparency burden. It does well by disclosing readiness probing (/health), exponential backoff retry, and audit event capture (who/model/node). It does not spell out side effects beyond audit or return behavior, but the mutating start/stop/restart nature is explicit.

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 packs the full behavioral contract into a single dense, front-loaded sentence. The key lifecycle concept appears first, followed by the most important behaviors; nothing is redundant.

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 10-parameter, four-action tool with full schema coverage, the description is nearly sufficient. It captures lifecycle behavior, readiness/retry, and audit semantics. The only notable omission is what each operation—especially 'status'—returns, since there is no output schema.

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 coverage is 100%, so the baseline is 3. The description adds some cross-parameter context—e.g., weights_dir is needed for start/restart and all backends expose an OpenAI-compatible endpoint—but it does not need to compensate for schema gaps.

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 '推理服务生命周期' and immediately specifies the resource (inference endpoint) and the four operations (start/stop/restart/status), plus the supported backends. This clearly separates it from training-oriented siblings like train_status or train_list, even without naming them.

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 communicates clear context: use this for deployment-lifecycle actions on an inference service, with start/restart tied to a weights directory. It does not explicitly state when not to use it or name an alternative, but the lifecycle framing is enough to route the agent correctly.

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