Read topic performance

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

Annotations already cover readOnly, non-destructive, idempotent, closed-world, but the description adds real context: required scopes (analytics:read, luca:read), redacted data sensitivity, and the important semantics that an empty items list is expected until topics are extracted rather than a signal of zero performance. The idempotency note is about idempotency keys rather than operation semantics and is slightly at odds with idempotentHint=true wording, but it is not a true contradiction.

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-loads the rollup and its metrics, then layers defaults, state semantics, and auth in short sentences. Slightly boilerplate-heavy (operation path, idempotency line) but each line carries information.

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?

An output schema exists so return shape need not be explained, and the description still covers scope, sensitivity, default week behavior, workspace override, and the empty-result state. Complete enough for a read-only aggregate tool, with only sibling differentiation left implicit.

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 only 67%, and the undocumented parameter is weekStart; the description compensates by explaining its default-to-most-recent-week behavior. The workspace override parameters are documented in the schema, so the remaining description text adds marginal value there.

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?

Names a specific verb (weekly rollup) and resource (normalized content topic) and enumerates the returned metrics (post count, engagement rate, leads/bookings/revenue per theme). It does not explicitly distinguish itself from close siblings like luca_analytics_content_performance or luca_analytics_content_attribution, which an agent could easily confuse with a topic-level rollup.

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 useful operating context (weekStart defaults to the most recently computed week; workspaceId/workspaceSlug override the configured default workspace) but never says when to prefer this tool over the sibling content/attribution analytics tools. Usage is implied rather than stated.

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