get_analytics_report

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

With no annotations, the description fully discloses behavioral traits: default aggregated metrics, effect of raw=true, behavior of granularity (monthly vs daily), and the misleading mix without it. It also notes the return type differences and prerequisites.

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 well-structured and front-loaded with the main purpose. However, it is a bit lengthy; a slightly more concise presentation while retaining all critical information could improve it.

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 (7 parameters, no output schema, no annotations), the description covers all necessary aspects: how to use, pitfalls, required inputs, and data nuances. It is fully complete for an AI agent to select and invoke the tool correctly.

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?

Despite 100% schema coverage, the description adds substantial meaning beyond the input schema: explains the raw parameter's effect, clarifies the importance and semantics of granularity, and provides context for dates. This greatly aids the agent in correctly invoking the tool.

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 clearly states that this tool retrieves analytics report data for an app and lists specific categories (APP_STORE_ENGAGEMENT, COMMERCE, etc.), which differentiates it from sibling tools like get_downloads_summary and get_sales_report.

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?

Provides explicit guidance on always setting granularity, explains the two-call strategy for full data, mentions required appId from list_apps, and warns against omitting granularity. This far exceeds the minimum requirement for usage guidelines.

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