Resolve a person's LinkedIn URL

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

Annotations only cover read-only/open-world/non-idempotent safety traits; the description adds substantial unannotated behavior — the 0.8 confidence threshold and clear-lead rule for 'found', the ambiguous/not_found/blocked taxonomy, per-event billing with free blocked rows, token requirements, and error-with-run-ID failure mode. This is exactly the context annotations cannot supply.

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 lead sentence is front-loaded with purpose and mechanism, followed by return fields, batching, and cost/auth constraints in a logical order with no filler. It is a single dense paragraph whose return-field enumeration runs long, slightly hurting scanability.

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?

With no output schema and no siblings, the description fully covers what the agent needs: return fields (linkedin_url, match_status, match_confidence, name_match, company_match, headline, snippet, followers, runner-up), failure behavior, billing model, and auth. Nothing required to call it correctly is missing.

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 every parameter is already documented in structured form; the description mainly restates the persons batch limit and cost behavior rather than adding parameter-level meaning. With the schema doing the heavy lifting, baseline 3 applies — it does not compensate for anything missing because nothing is missing.

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?

States a specific verb and resource (find a person's LinkedIn profile URL) and names the exact inputs it derives from (name plus company domain or name). It also discloses the mechanism (Google result pages via the Apify SERP proxy, LinkedIn never opened), so an agent knows both outcome and method without opening the schema.

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?

Gives clear operating context: batch up to 500 persons, requires an APIFY_TOKEN, one billed event per person with blocked/error rows free, and max_charge_usd can cap spend. It does not state exclusions or a counter-case (e.g., when not to use versus manual lookup, or how allow_name_only interacts with ambiguous results beyond a schema note), so it falls short of a 5.

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