test_docker_build

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

No annotations are provided, so the description carries full burden. It discloses dry-run behavior and the condition for real execution, but does not mention side effects (e.g., image creation), network access, permissions needed, or output format. This is adequate but leaves gaps for an AI agent.

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?

A single sentence conveys the primary purpose and a key conditional (dry-run). Every word adds value, and the information is front-loaded. No redundancy.

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 one parameter, no annotations, and an existing output schema (not shown), the description covers the essential action and the dry-run nuance. It could mention that the tool returns test results, but with output schema present, the agent can infer that. Almost complete for a simple tool.

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?

With 0% schema description coverage, the description should compensate, but it does not explain the single parameter 'image_tag'. The name is somewhat self-explanatory, but the agent would benefit from knowing the expected format (e.g., 'tag' vs full URL) or its role in the command.

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 specifies the action ('Run docker build + internal pytest tests') and the target ('an image'). It also adds context about dry-run behavior, which differentiates it from sibling tools like push_docker_to_registry that only push without testing.

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 provides clear context for when a real execution occurs vs dry-run ('dry-run unless local_python+docker configured'), but does not explicitly state when to use this tool over alternatives. However, the sibling list suggests distinct purposes, and the description implies use for testing rather than deployment.

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