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

Annotations already declare readOnlyHint=true and idempotentHint=true, so the safety profile is covered. The description adds meaningful behavioral context beyond annotations: it requires authentication, is read-only with no cost, consolidates multiple app contexts into one call, and prevents dataset guessing. It does not detail potential size limits or failure modes, but the core behavioral traits are well disclosed.

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 longer than average and includes some stylized claims ('THE first call', 'no dataset guessing'), but the content is front-loaded with the concrete resource and return items, and nearly every clause carries selection-relevant information. The format and drill-down guidance justify the length.

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 tool with no output schema, the description provides a detailed inventory of what is returned: every activated/connected app, platforms, connector-backed tables with dataset_id/slug and row count and last refresh, saved artifact list, and a ready-to-run query example. It also states authentication requirements, read-only behavior, and appropriate next-step tools, making it complete for agent invocation.

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 is format, and the input schema covers it completely with enum values, defaults, and descriptions for 'toon', 'compact', and 'json'. Since schema_description_coverage is 100%, the description does not need to add parameter detail, so baseline 3 is appropriate.

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 clear verb and resource: it returns the calling user's entire workspace in one call, enumerating apps, platforms, connector-backed tables, saved artifacts, and a runnable query example. It also differentiates from siblings by naming get_app_context, get_app_artifact, and query_dataset, making its scope unmistakable.

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 is explicit about when to use it: 'THE first call when a user references "my <app>", "my dashboard", "my report" or asks what they have on autario'. It also routes to alternatives, saying to drill down with get_app_artifact(app_id, slug) or query_dataset(dataset_id) for rows, and explains that it replaces per-app get_app_context calls.

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