host_check

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

With no annotations provided, the description fully discloses what the tool reports: Docker/containerlab availability, versions, allowed platforms, locally loaded images, and validate anchor devices. It does not mention side effects, but as a diagnostic tool, side effects are unlikely.

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 extremely concise: two sentences with no wasted words. The first sentence is a clear call-to-action, and the second enumerates the diagnostic outputs.

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 no-parameter diagnostic tool, the description covers its purpose, usage timing, and output details. An output schema exists, so the description need not detail return values, making it complete.

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 input schema is fully covered. The description adds value by explaining what information the tool gathers and reports, going beyond the trivial schema.

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 diagnoses host lab readiness and should be run first when failures occur. It distinguishes itself from sibling tools like generate_topology and validate_in_lab by positioning itself as a first diagnostic step.

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 explicitly advises running this tool 'first when anything fails,' providing clear when-to-use guidance. It lacks explicit when-not-to-use instructions, but the context and sibling list imply its role as a preliminary check.

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