li_get_leadgen_form_performance

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

No annotations are provided, so the description carries the full burden. It discloses the breakdown by creative and computed metrics, which aids transparency. However, it does not mention non-obvious behaviors like pagination, rate limits, authorization needs, or data freshness, which would be helpful for an agent.

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 extremely concise: two sentences that front-load the action and metrics, followed by use cases. No redundant or unnecessary information. Every sentence is purposeful and 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 no annotations or output schema, the description is fairly complete: it explains what metrics are returned and provides use cases. However, it leaves ambiguity about how the breakdown by creative is structured and does not explain the role of ad_account_id or the default behavior of date parameters. Still, it covers the core purpose well.

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 50% (2 of 4 parameters have descriptions). The description does not add information for the undocumented parameters (end_date, ad_account_id), nor does it clarify parameter usage beyond what the schema provides. It does not compensate for the missing schema descriptions, leaving the agent unclear about these parameters.

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 fetches LinkedIn Lead Gen Form performance analytics broken down by creative, listing specific metrics (impressions, clicks, opens, submissions, spend, and computed metrics like formOpenRate). It also differentiates from siblings like li_get_leadgen_forms (which lists forms) and li_get_leadgen_responses (which gets individual responses) by focusing on performance analytics.

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 explicit use cases: 'identify high-performing LGF creatives, diagnose drop-off between form open and submission, or compare cost-per-lead across campaigns.' It implies when to use the tool but does not specify when not to use or directly compare with alternatives. However, the context is clear.

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