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Dev-Anandhan

LinkedIn MCP Server

by Dev-Anandhan

search_people

Search for people on LinkedIn by keywords and optional location. Input queries like 'product manager at Meta' or 'ML engineer London'.

Instructions

Search for people on LinkedIn.

Args: keywords: Search keywords (e.g., 'product manager', 'ML engineer at Meta') location: Optional location filter (e.g., 'London', 'Berlin')

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsYes
locationNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault

No arguments

Behavior2/5

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

No annotations are provided, so the description bears full responsibility for behavioral disclosure. It fails to mention authentication needs, rate limits, or any behavioral traits beyond the search action.

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 very short and front-loaded, with no wasted words. It follows a docstring format, making it easy to parse, though slightly more structure could improve readability.

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?

Given the existence of an output schema, the description doesn't need to explain return values. However, it omits details like pagination, sorting, or result limits, leaving gaps for a search tool.

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

Parameters4/5

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

With 0% schema description coverage, the description compensates by providing concrete examples for both keywords and location, adding meaningful usage context beyond the bare schema definitions.

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 'Search for people on LinkedIn' with a specific verb and resource. It provides example keywords, distinguishing it from sibling tools like get_person_profile which targets a specific person.

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 through examples but does not explicitly state when to use or avoid this tool, nor does it mention alternatives among siblings. Basic context is present but no exclusions.

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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