train_doctor

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

The description fully discloses what checks are performed (docker, GPU, image) and what is reported (booleans, hints, training data root, HF_TOKEN). With no annotations, it carries the full burden and does so well, though it could mention if any side effects occur.

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 highly concise with two sentences covering all key aspects: checks, return type, and additional reports. No unnecessary words.

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?

Given no output schema, the description adequately explains return values (booleans, hints) and additional info. It covers the main functionality but could mention error handling or assumptions about the environment.

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?

The tool has zero parameters, so the baseline is 4. The description does not need to add parameter details; it appropriately describes the tool's operation without parameters.

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 clearly states the tool's purpose: performing a preflight check including docker daemon, GPU passthrough, trainer image build, and reporting booleans and hints. This distinctly separates it from sibling training tools like train_start or train_bootstrap.

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?

The description implies usage before training but lacks explicit guidance on when to use this tool versus alternatives. No exclusions or prerequisites are mentioned, making it adequate but not explicit.

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