get_linkedin_user_reactions

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It only states what the tool does and the URN format, lacking details on permissions needed, rate limits, error handling, response format, or whether it's a read-only operation (implied by 'Get' but not explicit). For a tool with potential API constraints, this is insufficient transparency.

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, efficient sentence that front-loads the core purpose and includes a critical example for the URN. There is no wasted verbiage, and it directly addresses the essential information needed to invoke the tool, making it highly concise and well-structured.

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 lack of annotations and output schema, the description is incomplete for a tool with three parameters and no behavioral context. It doesn't explain what 'reactions' entail (e.g., types, data structure), how results are returned, or any operational constraints. For a social media data tool, this leaves significant gaps in understanding its full context and usage.

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 the input schema fully documents parameters (urn, count, timeout). The description adds minimal value by reiterating the URN format example from the schema but doesn't provide additional context like URN sourcing, count/timeout implications, or default behaviors. This meets the baseline for high schema coverage without enhancing parameter understanding.

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's purpose: 'Get LinkedIn reactions for a user by URN'. It specifies the verb ('Get'), resource ('LinkedIn reactions'), and target ('user by URN'), making the function unambiguous. However, it doesn't explicitly differentiate from sibling tools like 'get_linkedin_post_reactions', which targets posts rather than users, though this distinction is implied by the name.

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 provides no guidance on when to use this tool versus alternatives. It mentions the URN requirement but doesn't clarify scenarios for choosing this over other LinkedIn tools (e.g., 'get_linkedin_user_posts' or 'get_linkedin_post_reactions'), prerequisites like authentication, or limitations such as data availability. This leaves the agent without contextual usage cues.

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