Get conversion stats

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

With no annotations, the description carries the full behavioral burden. It discloses that conversion data is derived from tracked content views, which is meaningful, but it does not mention time scope, aggregation period, data freshness, or that this is a read-only operation. Some transparency, but not exhaustive.

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?

A single, front-loaded sentence defines the metric, gives examples, and states the derivation source without filler. Every word earns its place.

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?

The description explains what is returned and gives example conversion types, but with no output schema and no annotations, the agent still lacks the complete list of possible conversion types and any time or filter semantics. Acceptable for a simple no-param tool, but not fully 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?

The tool has zero parameters, so schema coverage is trivially 100% and there is no parameter detail to supplement. With no input surface, the baseline 4 for zero parameters 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 states a specific verb and resource: get conversion stats, and precisely defines the metric as counts and rates by conversion type, with concrete examples (newsletter signup, consultation request). This clearly differentiates it from sibling analytics tools like get_unique_visitors_count and get_daily_views.

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 implies the tool is for conversion-focused analytics, but it provides no explicit when-to-use guidance or contrasts with alternatives like get_content_analytics or get_content_insights. An agent can infer the domain, but not the decision boundary between this and other analytics tools.

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