LinkedIn MCP Server

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Search Companies

search_companies

Read-only

Search LinkedIn for companies by keyword to find prospects, research industries, or identify organizations matching specific terms.

Instructions

Search for companies on LinkedIn.

Tool call template

Replace each placeholder with a value matching the input schema. Only required parameters are included; add optional parameters as needed.

{
  "id": 1,
  "jsonrpc": "2.0",
  "method": "tools/call",
  "params": {
    "arguments": {
      "keywords": "<keywords>"
    },
    "name": "search_companies"
  }
}

Input Schema

Name	Required	Description	Default
`keywords`	Yes	Search keywords (e.g., "fintech", "anthropic", "electric vehicles")

Output Schema

Name	Required	Description	Default
No arguments

Schema Changelog

Changes observed during successful MCP inspections.

First observedv4.26.0Sep 28, 2026

TDQS

B3.1/5.0

Behavior2/5

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

The description discloses no behavioral traits beyond what 'search' and 'LinkedIn' naturally imply. Annotations already declare readOnlyHint and openWorldHint, but the description adds no further context about result limits, matching behavior, or ordering. It is not contradictory, but it is also not informative beyond the basic action.

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 a single compact sentence that wastes no words and immediately states the core action and target resource. It is appropriately sized for a simple tool.

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?

For a one-parameter tool with an output schema and safety annotations, the description is minimally adequate. However, it does not clarify edge behavior, result scope, or how it differs from nearby company-related tools, so an agent may still need to infer intent.

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

Parameters3/5

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

The input schema already fully documents the only parameter, keywords, with a useful example. The description adds no additional semantic detail beyond the schema, so a baseline score of 3 is appropriate.

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 identifies the verb ('Search') and resource ('companies on LinkedIn'), so an agent can understand the basic function. However, it does not differentiate itself from sibling tools like get_company_profile or search_people, leaving some ambiguity about scope or result type.

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

Usage Guidelines2/5

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

There is no guidance on when to use this tool instead of search_people, get_company_profile, or get_company_employees. The description implies keyword-based discovery but provides no explicit context, prerequisites, or exclusions.

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