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bhaktatejas922

unipile-linkedin-mcp

search_companies

Find LinkedIn companies using criteria like keywords, industry, location, headcount, and job offers. Get paginated results.

Instructions

Search for companies on LinkedIn.

Use get_search_params() to find valid IDs for industry and location filters.

Args: keywords: Company name or description keywords industry: List of industry IDs location: List of location IDs (headquarters) headcount_min: Minimum employee count headcount_max: Maximum employee count has_job_offers: True to find companies currently hiring limit: Max results per page (1-50, default 25) cursor: Pagination cursor from previous response

Returns: Search results with company profiles and pagination cursor

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
keywordsNo
industryNo
locationNo
headcount_minNo
headcount_maxNo
has_job_offersNo
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 parameters and return values, including pagination, but does not disclose potential side effects, rate limits, or authentication requirements. Lacks depth for a complete behavioral picture.

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?

Description is concise with a brief purpose, a practical usage hint, and a clean Args/Returns format. Every sentence adds value, and the structure is front-loaded for quick comprehension.

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?

Covers all 8 parameters and explains return value (search results and pagination cursor). Lacks explicit mention of search scope (e.g., worldwide vs. regional) or edge cases, but is sufficient for an agent to use the tool effectively.

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?

Input schema has 0% parameter descriptions, but the description's Args section compensates fully by explaining each parameter's purpose (keywords, industry, location, headcount filters, has_job_offers, limit, cursor). This adds significant meaning beyond the schema's 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?

Description clearly states 'Search for companies on LinkedIn', specifying the action and target. It distinguishes from sibling tools like search_people and search_posts which have different targets.

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?

Provides explicit instruction to use get_search_params() for valid industry/location IDs, guiding the agent on prerequisites. Could add more alternatives (e.g., 'for detailed profile, use get_company_profile'), but the context from siblings is adequate.

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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