get_known_good

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

No annotations are provided, and the description only states it returns data 'if any'. It does not disclose whether the tool is read-only, what happens on missing data, or any side effects, leaving behavioral assumptions ambiguous.

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 a single front-loaded sentence of 13 words, efficiently conveying the core purpose without any fluff.

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 the complexity (3 parameters, 0% coverage, no annotations) and the presence of an output schema, the description still lacks context on what defines a 'known good' entry, how the lookup works, and what the rendered config includes, making it insufficient for a first-time user.

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 clarify parameter meanings. It mentions 'module+platform' but does not explain the parameters, particularly netlab_version which is optional, nor does it provide examples or constraints.

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 that the tool returns a previously lab-passed topology and rendered config for a given module and platform. It effectively distinguishes this from sibling tools like generate_topology (which creates new) and render_config (which may not be limited to known-good).

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 for retrieving known-good results, but does not explicitly state when to use this tool versus alternatives, nor does it provide conditions or when not to use it.

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