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

readOnlyHint=true already tells the agent this is a safe read, so the description only needs to add context. The Atlas endpoint reference and the human-readable-slug note are minor additions; nothing is said about what the dataset payload contains or behavior when a slug is not found.

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 front-loads the action and keys, followed by a short API reference. No filler, though the endpoint citation is of marginal value to an agent.

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 a fully covered schema and a readOnly annotation, the description is nearly sufficient. It omits what the returned dataset looks like and error behavior for unknown slugs, which with no output schema leaves a small gap.

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%, so both parameters are already documented. The description reinforces that the two strings form a human-readable path (org slug + dataset slug) but adds no formatting, casing, or validation detail 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?

States a specific verb (fetch) and resource (dataset) plus the exact lookup keys (organisation slug + dataset slug). It implicitly contrasts with a UUID-based lookup, which distinguishes it from the sibling get_dataset, but the sibling is never named outright.

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?

"usually easier than finding the UUID" gives a clear reason to prefer this tool over the UUID-based alternative. There are no explicit exclusions or failure conditions, but the routing signal is present.

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