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

Annotations already mark the tool readOnly and non-destructive, and the description adds important behavioral guardrails: no LinkedIn scraping, no automated connection or messaging, and mandatory human review. This goes beyond the annotation fields by disclosing compliance-relevant constraints and the tool's limited, advisory role.

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 compact, front-loaded with the core action, and every sentence carries meaning. It efficiently states what is produced, the decision purpose, and the safety limits without redundant phrasing.

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 description conveys the tool's role and constraints, and an output schema exists so return values need not be described. However, with 10 optional parameters and low schema coverage, the description does not sufficiently help an agent determine which parameters are relevant for a given research scenario or how this step fits into the broader talent-scout workflow.

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 only 40% and the tool has 10 parameters, yet the description adds no parameter-level guidance. Critical fields such as role_lane, company_name, company_target_id, campaign_id, and limit are left unexplained by both the description and partially by the schema, so an agent has limited basis for choosing correct input values.

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 ('Generate') and identifies a distinct resource ('public contact-research labels and safe LinkedIn search queries'). It also clarifies the downstream purpose—helping the account owner decide who to review manually—which separates it from broader talent-scout tools like pipeline or outreach drafting.

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 implies when to use the tool: when contact research is needed for human review. It also states firm exclusions (no scraping, no auto-connect, no auto-message), but it never names alternatives or explains when a sibling tool such as talent_scout_scan_company_for_roles or talent_scout_daily_pipeline would be more appropriate.

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