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

Annotations already provide the safety profile (readOnlyHint=true, idempotentHint=true, destructiveHint=false), so the description only needs to add context beyond that. It adds that the result is the full text of a published article including FAQs, but it does not describe error behavior or access edge cases. This is similar to other annotation-backed read tools.

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?

One tight sentence with no filler. The verb, resource, scope, access key, and content expectations are all front-loaded. Nothing could be removed without losing information.

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 one-parameter read tool with a complete schema, strong annotations, and a provided output schema, the description is fully sufficient. It specifies how to identify the article and what content to expect.

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?

Schema description coverage is 100%: the single slug parameter includes a type, minLength, and a concrete example. The description adds no further meaning about the parameter, so the baseline of 3 applies.

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 states a specific verb ('Get'), the exact resource ('full text of one published article from The Revenue AI Report'), and the access mechanism (by its slug). It also includes return-content highlights ('body paragraphs and FAQs'), which distinguishes it from siblings such as get_publication_info and search_publications.

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 clearly conveys when to use this tool: to retrieve a single article's full text given a known slug. It does not explicitly list when not to use it or name metadata-focused alternatives, but the context is unambiguous and no exclusion is critical.

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