Get revenue

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

Beyond readOnlyHint annotation, description adds behavioral guarantees: it's a point-read (never a scan), implying cheap and consistent. It also explains behavior for empty revenue (returns zeros, not error). No contradiction with annotations.

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?

Description is three sentences with no wasted words. Front-loaded with main purpose, followed by return details and usage examples. Perfectly concise.

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?

No output schema, but description partially compensates by listing return fields. Covers behavior for zero revenue. However, does not specify exact output format or structure, leaving some inference needed. Adequate for a simple read tool.

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%, so baseline is 3. Description reinforces the relationship between granularity and days but adds minimal new parameter-level info beyond schema. It does provide high-level context for how parameters interact.

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?

Description clearly states 'Get an app's recurring revenue' with specific verb and resource. It specifies it's a point-read, not a scan, and lists exact return fields (MRR, customer count, per-rail split). This differentiates it from siblings like draw_user_growth or get_customer.

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?

Description explicitly states use cases: 'what's our MRR', 'how many paying customers', or revenue-trend questions. It also notes that zero revenue returns zeros, not an error. However, it does not explicitly mention when not to use it or name alternative tools.

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