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Get LinkedIn Company

linkedin_get_company
Read-onlyIdempotent

Get a LinkedIn company profile. Pass identifier (a LinkedIn company URL, slug, or numeric ID) when you know it. If you only know the company name, pass name instead of guessing a slug: LinkedIn slugs often differ from the name. A name returns the company when it is a clear match, otherwise a short list of candidates to choose from.

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
nameNoThe company name (e.g. "Acme Corporation"), when you do not know its LinkedIn URL. Searches LinkedIn through your connected account. Use this or identifier, not both.
identifierNoLinkedIn company URL (e.g. https://www.linkedin.com/company/acme), company slug (e.g. "acme"), or numeric ID. Use this or name, not both.

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observed

TDQS

A4.3/5.0
Behavior4/5

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

Annotations already cover the safety profile (readOnly, idempotent, non-destructive, openWorld). The description adds real behavioral context beyond them: a name lookup returns a single company on a clear match or a short candidate list otherwise, which tells the agent to expect an ambiguous result shape. It does not cover rate limits or pagination, keeping it short of a 5.

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?

Three tight sentences, front-loaded with the primary action before the conditional lookup guidance. No filler or restatement of the tool name.

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?

There is no output schema, so the description must carry return-value burden, and it does explain the single-result-vs-candidate-list behavior. It never describes what a returned profile contains, but for a straightforward read tool the coverage is nearly sufficient.

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 100%, so the baseline is 3, and both parameters are already documented with their formats ('Use this or name, not both'). The description adds genuine meaning on top: the rationale for preferring identifier and the failure mode of slug guessing, which the schema does not explain.

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?

States a specific verb and resource ('Get a LinkedIn company profile') and explains the two lookup modes, which is more than a tautology. However, it never names the adjacent sibling linkedin_bulk_get_companies, so an agent must infer the single-vs-bulk distinction on its own.

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

Usage Guidelines5/5

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

Gives explicit conditional routing: pass identifier when the URL/slug/ID is known, pass name when only the name is known, and explicitly warns against guessing a slug because LinkedIn slugs often differ from the name. The rule for choosing between the two parameters is fully specified.

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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