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

Goes beyond the annotations by disclosing the authentication requirement (API key or OAuth), that the call is free, and that results are guaranteed to match the user's own datasets and saved views (anti-hallucination guarantee). Missing only scale/limit behavior, e.g. whether the app list is truncated or paginated when a user has many apps.

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?

Front-loaded with the core value proposition and the usage trigger appears early, with drill-downs and auth at the end. Some waste is visible in the long parenthetical platform enumeration (Google Search Console, GA4, ... LinkedIn, Bing) and the pipe-delimited run-on structure, which reduces scannability.

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?

There is no output schema, and the description compensates by enumerating the returned fields (platforms, dataset_id/slug, row count, last refresh, artifact list, query example, shared_with_you fields). Combined with the auth note and the explicit next-step tools, an agent has everything needed to call it correctly and interpret the result.

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% and the single optional 'format' enum is fully documented in the schema (toon/compact/json, including the note that REST always returns JSON). The description never mentions output format, so it adds no meaning beyond the schema — baseline 3 applies.

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+resource and enumerates exactly what the payload contains (apps, platforms, connector-backed tables with dataset_id/slug/row count/refresh, saved artifacts, shared_with_you tables). The 'YOUR data-app workspace in ONE call' framing and the explicit distinction from per-app get_app_context round-trips make it unmistakable against siblings like list_apps or get_app_context.

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?

Gives explicit trigger phrases ('my <app>', 'my dashboard', 'my report', 'what they have on autario') and names the correct drill-down alternatives, get_app_artifact(app_id, slug) and query_dataset(dataset_id). It does not address when to prefer the sibling list_apps instead, which is the one adjacent choice an agent must make.

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