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Search People

search_people
Read-only

Find LinkedIn members by keyword, location, or company. Filter results by connection degree or current employer to identify relevant professionals.

Instructions

Search for people on LinkedIn.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYesSearch keywords (e.g., "software engineer", "recruiter at Google")
locationNoOptional location filter (e.g., "New York", "Remote")
networkNoOptional connection-degree filter. Each element is one of "F" (1st-degree), "S" (2nd-degree), "O" (3rd-degree and beyond). Example: ["F"] to only return 1st-degree connections.
current_companyNoOptional current-employer filter. LinkedIn's currentCompany facet only filters on the numeric company URN id (e.g. "1115" for SAP); plain company names are accepted by the URL but ignored by LinkedIn and return the unfiltered result set. Look up a company's URN via get_company_profile -- it is exposed under references["about"]. For company-wide employee demographics (location/education/function breakdown) plus a slug-based lookup, use get_company_employees instead.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

Description adds no behavioral context beyond the annotations. Annotations already mark it as read-only and open-world. No mention of rate limits, pagination, or result truncation. Given annotations exist, more context could be added but isn't.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Extremely concise single sentence. No wasted words, but could be slightly more informative without losing conciseness.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness3/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

With output schema present and good parameter descriptions, the description is minimally complete. However, a search tool might benefit from mentioning result limits or the default behavior of filters, which is absent.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so the schema already explains parameters well. The description does not add meaning beyond the schema; it just restates the purpose. Baseline 3 is appropriate.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

The description clearly states the tool searches for people on LinkedIn, specifying the resource ('people') and action ('search'). It's unambiguous, though it could be more specific about the search scope or differentiate from siblings like get_person_profile.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines2/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

No guidance on when to use this tool versus alternatives like get_person_profile or search_companies. The description does not mention context, prerequisites, or when not to use it.

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

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