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

Annotations already declare readOnly/idempotent/non-destructive/openWorld, and the description still adds substantial behavioral context beyond them: the 15-minute provenance_token validity window, that it may be passed on any replica, that values must be mapped to exact source_path, the consequence of an invalid receipt (unverified_model_data labeling), and a data-accuracy contract forbidding invented metrics. This is unusually rich disclosure for a read-only connector.

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 purpose is front-loaded, but a large share of the text is downstream rendering/provenance policy that belongs to chart_render rather than this connector, which inflates the definition. The accuracy contract is valuable, yet the block is long and mixes tool selection guidance with response-formatting rules.

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 read-only, multi-action connector with no output schema, the description covers the analytical purposes, the action set (via schema), provenance rules, and the data-accuracy contract. It is silent on the shape of returned values or data-availability caveats, but there is no output schema obliging it to explain returns, so the coverage is close to complete.

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 action enum and per-action params object are fully itemized in the schema, so the description's job here is minimal. It adds no date-format, pagination, or external_shopify_data syntax detail, so the 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?

The description names a specific verb and resource ('Cross-source analysis correlating Klaviyo email activity with web traffic, ecommerce revenue, and ad spend') and enumerates the analytic purposes (channel attribution, email-driven revenue, unified marketing analytics). It is clear what the tool does, but it never distinguishes itself from siblings like cross_source_ads_connector or klaviyo_connector, leaving the agent to infer the boundary.

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?

It states the contexts for use ('Use for channel attribution, email-driven revenue, and unified marketing analytics') and gives a downstream trigger (call chart_render when the user asks for a visual, trend, comparison, or recap). It does not say when NOT to use it or which sibling to prefer instead, so it stops short of full routing guidance.

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