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bhaktatejas922

unipile-linkedin-mcp

search_people

Find LinkedIn profiles by applying filters like keywords, location, industry, company, and connection degree. Get paginated results.

Instructions

Search for people on LinkedIn using Classic LinkedIn filters.

This is the standard LinkedIn search available to all users. Use get_search_params() to find valid IDs for location, industry, and company filters.

Args: keywords: Free text search (name, title, company, etc.) location: List of location IDs (use get_search_params to find IDs) industry: List of industry IDs company: List of current company IDs past_company: List of past company IDs network_distance: Connection degree [1, 2, 3] - 1=1st degree, 2=2nd degree, 3=3rd+ profile_language: ISO language codes (e.g., ["en", "fr"]) limit: Max results per page (1-50, default 25) cursor: Pagination cursor from previous response

Returns: Search results with profiles and pagination cursor

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsNo
locationNo
industryNo
companyNo
past_companyNo
network_distanceNo
profile_languageNo
limitNo
cursorNo
Behavior3/5

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

No annotations provided, so description carries full burden. It describes pagination via cursor and basic search behavior, but does not disclose rate limits, authentication needs, or potential side effects. Adequate for a read-only tool.

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?

Description is well-structured with Args and Returns sections, front-loaded with purpose. Slightly verbose but every sentence adds value.

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 no output schema, description explains returns ('Search results with profiles and pagination cursor'). All parameters described. Sibling tools provide context. Missing details like result count limits or error handling.

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?

Schema coverage is 0%, so description compensates well. It provides meaningful descriptions for all 9 parameters (e.g., 'Keywords: Free text search', 'network_distance: Connection degree [1,2,3]'), adding context beyond schema types and defaults.

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 'Search for people on LinkedIn using Classic LinkedIn filters', specifying the verb and resource. It distinguishes from siblings like 'search_companies' and 'search_people_sales_nav' by mentioning 'Classic LinkedIn filters', implying standard search.

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

Usage Guidelines4/5

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

Description guides use of 'get_search_params()' to find valid IDs for filters, provides parameter details, and states 'This is the standard LinkedIn search available to all users.' It gives context but lacks explicit when-not-to-use vs. siblings.

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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