List Doc Topics

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

Annotations already declare readOnly/idempotent/non-destructive, so the bar is lower; the description still adds real value by disclosing the exact return shape (Markdown index, one-line descriptions, endpoint counts, links to full reference and OpenAPI spec). With no output schema, this disclosure carries meaningful behavioral context.

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?

Three tight sentences: purpose, return payload, then the routing instruction to read_doc. Front-loaded with the most decision-relevant information and no filler.

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, read-only discovery tool with no output schema, the description fully covers what is returned and how it fits into the docs-reading workflow. Nothing an agent needs to call it correctly is missing.

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?

Zero parameters, so the baseline is 4; the description implicitly communicates that no input is required to enumerate the docs index. There is nothing further a parameter section could add.

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?

States a specific verb+resource ('List the available Porkbun API documentation topics') and immediately specifies the returned content — an index of per-topic pages with descriptions and endpoint counts. This clearly separates it from the sibling read_doc/search_docs tools.

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?

Explicitly prescribes ordering and the alternative: 'Use this first to discover what docs exist, then read_doc to read one.' The when-to-use condition and the follow-up tool are both named, leaving nothing to inference.

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