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

Annotations already declare readOnlyHint=true, idempotentHint=true and destructiveHint=false, so the safety profile is covered. The description adds non-obvious value beyond that by disclosing the return format (Markdown) for a single full write-up, which matters since no output schema exists.

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?

A single tight sentence with no filler. The essential information—what is returned, in what format, and how it is keyed—is front-loaded.

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 two-parameter read tool with an enum already documented in the schema and no output schema, the description covers purpose, format, and the slug's provenance well. Only the default-locale behavior and edge cases (e.g., unknown slug) are left unstated, which is minor.

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 coverage is 50%: locale is documented in the schema, but slug has no description. The description compensates by explaining that the slug is the identifier and where to obtain it (from list_case_studies), which is real semantic value beyond the bare string type.

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), a specific resource (the full write-up of one BeevR case study), the output format (Markdown), and the lookup key (slug). It clearly distinguishes itself from the sibling list_case_studies by returning a single full document rather than a list.

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?

It gives clear context for when to use this tool and explicitly points to list_case_studies as the source of the slug, establishing the correct call order. It does not state exclusions or when-not-to-use cases, but for a simple lookup the context is sufficient.

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