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

Annotations already establish the tool as read-only and non-destructive. The description adds meaningful behavioral context by describing the shape of returned dimensions (name vs label), the role of externalMetricIntegrations, and downstream computeStatus polling expectations. It does not disclose every possible response detail, but the added value beyond annotations is strong.

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 long and dense, but nearly every sentence carries a distinct operational instruction. It is front-loaded with the core purpose and call-first directive before moving into detailed domain rules. A bit of restructuring into clearer segments would improve scannability, but the length is justified by the tool's central role.

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?

Despite having no output schema, the description gives enough information about return contents and naming conventions for an agent to correctly consume the response. It also covers related workflows — search for dimensions, list_metrics for integrations, bqName usage for custom virtual dimensions, and computeStatus polling — so the agent is fully oriented for follow-up actions.

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 only parameter, slug, is already fully described in the input schema with clear guidance about omitting it for auto-detection. Schema description coverage is 100%, so the description does not need to add parameter semantics. The extensive domain guidance in the description applies more to downstream tool usage than to this tool's parameters.

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 opens with a specific verb and resource: "Get workspace context" and enumerates exactly what it returns — popular groupBy dimensions, recent dashboards, and connected external-metric integrations. It clearly distinguishes this tool from siblings by positioning it as the initial discovery/context tool and explicitly redirecting to search for dimension discovery and list_metrics for integrations.

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 gives an explicit invocation rule: "Call this FIRST at the start of every conversation." It also provides concrete when-to-use-alternatives guidance, such as using search with type ["dimensions"] to discover CEL field names and list_metrics with includeExternal for integration metrics. This is unusually actionable.

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