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get_person_profile

Retrieve LinkedIn profile data for a person by username, including experience, education, and other sections.

Instructions

Get a specific person's LinkedIn profile.

Args: linkedin_username: LinkedIn username (e.g., 'satyanadella', 'jeffweiner08') sections: Comma-separated list of extra sections to scrape. The main profile page is always included. Available sections: experience, education, interests, honors, languages, contact_info, posts Default (None) scrapes only the main profile page.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
linkedin_usernameYes
sectionsNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior3/5

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

With no annotations provided, the description carries the full burden. It mentions scraping behavior and that the main profile is always included, which adds useful context. However, it doesn't disclose rate limits, authentication needs, or potential side effects like data freshness or scraping constraints.

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?

The description is well-structured with a clear purpose statement followed by detailed parameter explanations. It's appropriately sized, though the sections explanation could be slightly more concise. Every sentence adds value without redundancy.

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

Completeness4/5

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

Given 2 parameters with 0% schema coverage and no annotations, the description does an excellent job explaining inputs. With an output schema present, it doesn't need to detail return values. However, for a scraping tool, it could better address behavioral aspects like rate limits or data reliability.

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

Parameters5/5

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

Schema description coverage is 0%, so the description fully compensates by explaining both parameters in detail. It defines linkedin_username with examples and sections with available options, default behavior, and format, adding significant meaning beyond the bare schema.

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

Purpose5/5

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

The description clearly states the specific action ('Get') and resource ('specific person's LinkedIn profile'), distinguishing it from sibling tools like get_company_profile or search_people. It precisely defines what the tool does without being vague or tautological.

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

Usage Guidelines3/5

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

The description implies usage by specifying it's for getting a 'specific person's' profile, but doesn't explicitly state when to use this versus alternatives like search_people. No guidance on prerequisites, exclusions, or comparative contexts with siblings is provided.

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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