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

There are no annotations, so the description carries the full burden of behavioral disclosure. It reveals performance characteristics ('Fast — hits the pre-aggregated reporting endpoint'), a hard constraint ('Date range required, max 30 days'), response nuance ('read daily_series in response'), and a prerequisite for valid event names. These details go well beyond the schema and give the agent a clear model of how the tool behaves.

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 front-loaded with a one-sentence summary, followed by a scannable USE WHEN bullet list and explicit alternative routing. Although it is longer than average, every section earns its place by resolving a distinct selection or invocation decision for an 11-parameter tool.

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?

Given the tool's complexity, full schema coverage, and presence of an output schema, the description is complete for an agent's needs. It covers purpose, when to use it, when not to use it, parameter semantics through examples, constraints, and prerequisites. No consequential gap remains for correct tool selection and 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?

Schema coverage is 100%, so the baseline is 3, but the description adds substantial value by translating user intent into parameter settings, e.g., dimensions=['source'] for traffic sources and connector='facebook' for delivery integration filters. It also documents the max-30-days date-range constraint, which the schema does not state. This exceeds the baseline and meaningfully helps the agent construct correct invocations.

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: 'Aggregated analytics broken down by dimension and/or time.' It also explicitly contrasts itself with siblings by stating it hits the pre-aggregated reporting endpoint rather than the raw events table, and it names get_summary, list_events, and list_sessions as distinct tools. An agent can immediately tell what this tool does and what it is not.

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 provides an explicit USE WHEN section with concrete natural-language examples mapped to parameter values, such as 'how many Purchases vs Leads?' → dimensions=['event_name'] and 'first-click vs last-click attribution' → attribution='first_click'. It also gives clear when-not-to-use guidance: use get_summary for a single-period snapshot, and prefer this over list_events/list_sessions for numeric/chart/ranking questions. It even states required prerequisites like calling get_container first for valid event names.

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