search_linkedin_profiles

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

With no annotations provided, the description carries the full burden of behavioral disclosure. It transparently explains the mechanism (DuckDuckGo search, not direct scraping), the no-account-risk aspect, the shallow data depth, the return structure (match_score, matched_signals), and the self-imposed daily request budget. This goes well beyond a simple search description.

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 moderately long but every sentence contributes value, covering purpose, limitations, and request budget. It is front-loaded with the primary function and does not waste words. The length is justified by the tool's complexity and the absence of annotations.

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 the tool has 9 parameters, no output schema, and no annotations, the description provides a thorough context: return shape (name/headline/company/location, match_score, matched_signals), data limitations, search engine caveats, and advice on result counts. It is sufficiently complete for an agent to decide and invoke the tool appropriately.

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?

The schema provides 100% description coverage for all 9 parameters, so the baseline is 3. The description does not add significant meaning beyond the schema, though it does provide context for max_results via the daily request budget and mentions the ranked output quality.

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 discovers public LinkedIn profiles via DuckDuckGo's public search, using a specific verb and resource. It also distinguishes itself from sibling tools by explicitly noting it does not log into or scrape LinkedIn directly.

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 clear context on when to use the tool (discovering public LinkedIn profiles) and when not to rely on it (needs verified enriched profiles or full work history). It also advises broadening criteria if few results are returned. However, it does not explicitly name an alternative tool, only suggests manual verification.

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