Search LinkedIn People

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

Annotations already declare readOnly/openWorld/idempotent/non-destructive behavior. The description adds valuable context beyond this: results are limited to 'visible' LinkedIn data, only one cursor page is returned, and name-to-ID resolution plus traversal safety bounds are intentionally private. This gives the agent useful operational expectations without contradicting the annotations.

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 two sentences and efficiently packs a lot of filter information without redundancy. The first sentence front-loads the main action and capabilities; the second adds behavioral constraints. It is slightly dense but still concise and scannable.

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 large nested-filter schema and presence of an output schema, the description provides adequate context: it summarizes the full range of filter types, states pagination is limited to one cursor page, and discloses private resolution/traversal bounds. It does not fully explain cursor-based pagination flow or clarify deprecated parameters, but these gaps are partially covered by schema descriptions.

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 low (13%), so the description carries more burden. It does add meaning by clarifying that the query accepts natural-language or Boolean keywords and that certain name fields are exact keyword filters. However, it omits important parameter details like the deprecated max_results alias, title_keywords semantics, and the distinction between IDs and names across the nested filters, leaving the agent to rely heavily on the schema.

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 opens with a specific verb+resource ('Search visible LinkedIn People results') and enumerates a wide range of filter dimensions. It clearly distinguishes this from sibling tools like linkedin.people.get (retrieval) and linkedin.connections.search (connections-specific) by emphasizing network-wide people search with many filter types.

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 gives clear context on what the tool does and notes that it returns one cursor page, which helps set pagination expectations. However, it does not explicitly state when to prefer this over alternatives like linkedin.people.get or linkedin.companies.search, nor does it mention any exclusions.

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