linkedin_import_search_results

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

With no annotations, the description bears the full burden. It discloses the normalization behavior but omits side effects such as whether existing Leads are overwritten, duplicates are created, or the operation is reversible. For a data-ingestion tool this is a significant gap.

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?

A single, front-loaded sentence with no redundant words. It efficiently conveys the action, input formats, and result.

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?

The tool is simple and the schema covers both parameters, but the description does not mention return value, failure modes, or pipeline fit. Given no output schema and no annotations, some additional context would help an agent invoke it confidently.

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%, and both parameters are described in the schema. The description adds no meaningful detail beyond the schema's own parameter descriptions, so it stays at the baseline for high coverage.

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 uses a specific verb ('Ingests') alongside a clear resource ('JSON/CSV search result objects or profile URL lists') and outcome ('normalized Lead entities'). This clearly differentiates it from the analysis/ranking siblings, none of which import data.

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 intended usage is implied by the description: use this when you have raw search results to import as Leads. However, there are no explicit alternatives, exclusions, or conditions compared with siblings like linkedin_analyze_search_results.

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