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ChimbuezeDavid

Universal LinkedIn MCP Server

search_people

Find LinkedIn professionals by keywords, location, or current company. Retrieve matching profiles to identify potential candidates or contacts.

Instructions

Search for professionals on LinkedIn through your authenticated account.

Args: keywords: Search term (e.g. name, title, skills). location: Optional location filter (e.g. 'San Francisco', 'United Kingdom'). current_company: Optional current company name. limit: Maximum results to retrieve (default: 10, max: 25).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
limitNo
keywordsYes
locationNo
current_companyNo

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
resultYes

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv0.1.0

TDQS

A4.1/5.0
Behavior3/5

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

There are no annotations, so the description carries the behavioral disclosure burden. It adds useful context like 'through your authenticated account' and the maximum result limit, but it does not explicitly state read-only behavior, rate limits, error conditions, or session prerequisites beyond the auth implication.

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

Conciseness5/5

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

The description is compact and front-loaded with the operation, followed by a tight parameter list. Every line is informative and nothing is redundant with the schema.

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?

An output schema is present, so return-value details are already covered. The description addresses all four parameters, marks optionality, gives the result cap, and mentions authentication. It does not explain how filters combine (e.g., AND vs OR), but that is a minor gap for a search tool.

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?

The schema only provides property titles with 0% description coverage, so the description's Args block is essential and fully compensates. Each parameter gets a clear semantic meaning with examples for keywords and location, plus the default and max for limit.

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 uses a specific verb ('Search') and resource ('professionals on LinkedIn'), and adds that this happens through the authenticated account. Siblings like view_profile, get_feed, and get_network_briefing are clearly different, so an agent can confidently select this as the people-search tool.

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 use case is implied by 'Search for professionals' and there is no other search tool among the siblings, so the intended context is understandable. However, the description gives no explicit when-to-use or when-not-to-use guidance, exclusions, or routing hints toward alternatives.

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