eds_get_experiments

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

With no annotations provided, the description bears full responsibility for behavioral disclosure. It indicates a read operation but does not mention data freshness, pagination, authentication needs, or potential side effects. The behavior is partially transparent but insufficient.

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 a single, front-loaded sentence that efficiently conveys the core purpose and key output details without extraneous 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?

Despite having no output schema, the description mentions three specific data fields (views, clicks, conversion rates). However, it lacks detail on the structure of the results (e.g., per experiment or aggregated) and does not clarify behavior for multiple experiments or missing data. Adequate but incomplete.

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% with basic parameter descriptions. The description adds context about output fields (views, clicks, conversion rates) but does not elaborate on the parameters themselves, only restating their purpose. This meets the baseline but adds limited semantic value.

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 specifies the verb 'Get', the resource 'A/B experiment results for an EDS site', and includes specific data fields (variant views, clicks, conversion rates). It differentiates from sibling tools that deal with pages, redirects, or logs.

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 retrieving A/B experiment results but provides no explicit guidance on when to use it over alternatives or any exclusions. There is no mention of prerequisite calls or comparison with similar tools.

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