get_related_v1_graph_datasets__provider___dataset__related_get

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

No annotations provided, so description carries full burden. It states it is public and uses graph algorithms, but does not clarify read-only nature, authentication needs, rate limits, or side effects. The term 'structural + semantic blended' is vague.

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?

Single sentence but includes redundant HTTP method and path already in tool name. Could be more concise by removing the GET path and 'Get Related' fragment. However, it is short and front-loaded with key info.

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?

With no output schema and minimal description, the tool behavior is underspecified. Missing details: response format, pagination, effect of limit, definition of structural+semantic blending. Not sufficient for an AI to reliably use 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?

Schema description coverage is 0%, and the description adds no meaning to the three parameters (provider, dataset, limit). No explanation of what each parameter represents or how limit affects results. Parameters are left entirely to their names and types.

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 the tool retrieves graph-powered related datasets using structural and semantic blending, distinguishing it from the sibling get_related_datasets which likely uses a different method. The verb 'Get' and resource 'related datasets' are specific.

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?

Implied usage via mention of 'graph-powered' and 'public', but no explicit guidance on when to use this over the sibling or other related endpoints. No when-not-to-use or alternative suggestions.

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