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

Annotations already establish a safe read (readOnlyHint true, destructiveHint false), and the description adds real behavioral context: answers come from cache as-is, and an out-of-date Data Set owned by you triggers a background refresh that the reply discloses. That side-effect disclosure is exactly the kind of context annotations cannot convey.

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?

Two dense sentences with the capability and return contents front-loaded, followed by the caching/refresh caveat. No filler, and the ordering puts the most decision-relevant information first.

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 single-parameter read tool with full schema coverage and no output schema, the description covers what comes back, the access scope, and the caching behavior. 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?

Schema coverage is 100%, so the single 'name' parameter is already documented. The description adds minor meaning by clarifying the name resolves within the caller's own or shared Data Sets, but offers no format, casing, or ambiguity-resolution detail beyond that.

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 and resource and enumerates what is returned: columns, indexes, freshness, and cached table name for a Data Set. It also pins the lookup key ('identified by its name'), which separates it from list-shaped siblings like dataset_list, though it never names an alternative explicitly.

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 implies when to reach for it (you have a Data Set name and want schema/metadata) and scopes it to Data Sets you own or that are shared with you. It gives no explicit guidance against dataset_list, dataset_query, or dataset_refresh, so the agent must infer the boundaries.

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