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

Annotations only cover safety/idempotency; the description adds substantial context they cannot: a 15-minute provenance-token validity window valid 'on any replica', the unverified_model_data fallback labeling, the mandatory attribution footer requirement, and a data-accuracy contract governing derived metrics. This is exactly the behavioral disclosure structured fields cannot carry.

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 purpose, then downstream routing, then the accuracy contract — a logical progression with no throwaway sentences. It is dense and somewhat long, but each sentence carries a distinct obligation (provenance, footer, data honesty) rather than restating structured data.

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 tells the agent what the tool returns (correlation values plus a possible provenance token and attribution footer) and how to consume them. For a nested-parameter, openWorld read connector, nothing needed 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 description coverage is 100% — the action enum and the nested params object are fully documented in the schema, including the per-action {customer_id, property_id, start_date, end_date} shape. The description adds only a loose paraphrase of the same correlations, 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?

States a specific verb+resource pair — 'Cross-source analysis correlating Google Ads spend with GA4 web traffic and revenue' — and names the exact platforms being joined. An agent can immediately distinguish this from the single-source google_ads_connector and ga4_connector siblings.

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?

Explicit trigger: 'Use when comparing ad spend to sessions, conversions, or ROAS across platforms', plus routing rules to chart_render for visuals. It gives strong positive guidance but never states when NOT to use it (e.g., single-source queries belong to google_ads_connector), so it falls short of explicit alternative routing.

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