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

With no annotations provided, the description carries the full burden of behavioral disclosure. It states the output format (JSON), which is useful, but does not disclose whether the operation is read-only, side-effect-free, or requires authentication. For a retrieval tool, this is a minor gap, but the description adds only minimal 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?

The description is a single, well-structured sentence that front-loads the action and resource, followed by the necessary enumeration. There is no wasted wording, and the list is essential for correct usage.

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 simple single-parameter tool with no output schema, the description covers the key information: what it returns (JSON) and the valid inputs. It does not mention error handling or authentication, but these are minor for a basic get operation. The description is largely sufficient.

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 schema provides a single 'name' parameter with no description, enum, or format (0% coverage). The description fully compensates by listing all eight valid dataset names, which gives the agent precise guidance on what values are acceptable. This goes far beyond the schema.

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 action ('Return') and the resource ('one dataset as JSON'), and explicitly enumerates the valid dataset names. This distinguishes it from siblings like claude_code_cost_anatomy and github_trending, which are purpose-specific 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?

The list of valid names provides clear context on which datasets can be retrieved with this tool, implicitly indicating that for these specific datasets, get_dataset is the correct tool. However, it does not explicitly state when to prefer a sibling tool or mention exclusions, so it falls short of an explicit when/when-not guidance.

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