generate_topology

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

The description discloses key behaviors: the output is parse-validated YAML, the module is inferred, and platform list order matters (DUT first). With no annotations provided, the description carries the full burden and covers the main behavioral aspects, though it doesn't mention any potential side effects or permissions required.

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 concise (two sentences plus brief elaboration) and front-loaded with the main purpose. Every sentence adds value: purpose, parameter guidance, and downstream usage. 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 the tool has 2 parameters, an output schema, and no nested objects, the description covers all necessary aspects: what the tool does, what inputs mean, and how to use the output. It is fully complete for an agent to select and invoke the tool correctly.

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 description adds significant meaning beyond the input schema: it explains that 'intent' is free text with examples, and 'platforms' is a NOS list with DUT-first ordering and an MVP set. Schema coverage is 0% but the description fully compensates, providing clear semantics for both 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: 'Turn an intent + target platforms into a netlab topology YAML (parse-validated).' It uses a specific verb ('Turn') and resource ('netlab topology YAML'), and distinguishes itself from siblings by mentioning that the output should be fed to 'render_config' or 'validate_in_lab'.

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 context by explaining that the output is intended for downstream tools ('render_config' or 'validate_in_lab'), but it does not explicitly state when to use this tool versus alternatives like 'list_examples' or 'query_compatibility'. It gives examples of intent but no exclusions.

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