scrape_person_profile

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

With no annotations, the description carries the full burden and does substantial work: it discloses that the tool mutates state ('The result is upserted into the people table'), that it preserves existing short-mode data while merging extended fields, and reveals the underlying provider and per-thousand cost ($4 vs $10 with email). This is exactly the kind of side-effect disclosure a scrape-named tool needs; it only omits edge cases like not-found profiles or failure behavior.

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 front-loaded: purpose first, then usage, then side effects, then pricing, then a clean Args block. It is slightly redundant in that cost appears both in the provider sentence and again in the with_email arg, but the overall structure is logical and every section earns its place.

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 covers purpose, usage triggers, the database side effect, costs, and all three parameters in depth, and an output schema exists so return values need no explanation. The main gaps are that it never differentiates this tool from the sibling scrape_profile and doesn't state prerequisites (e.g., whether the person must already exist in the people table before enrichment).

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 0%, so the description must fully compensate, and it does. url gets a full format explanation plus a concrete example (https://www.linkedin.com/in/williamhgates), with_email gets its behavior, cost implication, and default, and company_url gets its fallback logic ('uses the person's current company from Apify... falls back to the profile URL itself'). Every parameter is meaningfully explained.

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?

Opens with a specific verb and resource: 'Enrich ONE LinkedIn profile with full data' followed by an explicit field list (experience, education, skills, certifications, etc.). The deliberate 'ONE' scopes it against the plural sibling scrape_people, and the semantic of enrichment distinguishes it from the similarly named scrape_profile.

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?

Provides an explicit trigger condition: 'Use this when you need commonality hooks for outreach' with concrete examples (overlapping past employers, shared schools, mutual volunteer work, common languages). It gives clear when-to-use context but does not name alternative tools or state when not to use it, so it stops 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.