get_recommendations_v1_me_recommendations__kind__get

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

With no annotations, the description carries full burden. It discloses that it's a GET endpoint requiring authentication, returns recommendations based on activity, and describes cold-start behavior. However, it omits details about pagination behavior, error handling, rate limits, or response structure, which are important for safe invocation.

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 with a title line, a brief summary, a list of recommendation kinds, and an authentication note. It is front-loaded with key information. Minor redundancy like repeating 'Get personalized recommendations' could be removed, but overall it is efficient.

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 moderate complexity and lack of output schema, the description adequately explains the tool's purpose, parameters, and authentication. However, it does not describe the response format or pagination behavior. For an agent to reliably use pagination or interpret results, additional details are needed.

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 input schema covers 67% of parameters with descriptions (limit, offset). The tool description adds significant value for the 'kind' parameter by explaining each enum value (datasets, providers, users) and their meaning. This goes beyond the schema, which only lists the enum without descriptions.

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 the tool gets personalized recommendations for datasets, providers, or users based on user activity. It lists the three kinds and distinguishes from similar tools like 'discover' or 'search' by emphasizing personalization. However, it does not explicitly differentiate from siblings like 'discover_datasets' or 'get_related_datasets', leaving minor ambiguity.

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 explains the tool should be used for personalized recommendations and notes cold-start behavior for new users. It lists the recommendation kinds and authentication requirements. While it provides good contextual guidance, it does not explicitly state when not to use this tool or directly mention alternative tools.

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