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Get a LinkedIn company

linkedin_get_company
Read-only

Fetch a company's LinkedIn profile details using its URL, slug, or website domain. Get name, industry, employee count, and more for verified companies.

Instructions

Fetch one company's LinkedIn page: name, description, industry, employee count, headquarters, website, founding year, specialities, and the URN/numeric id you need for linkedin_search_people company filters. identifier accepts a company URL, the slug after /company/ (e.g. 'microsoft'), or a website domain like 'microsoft.com'; numeric ids and URNs are search-filter inputs, not fetch identifiers. Domains are resolved to a company and verified against that company's website: a domain identifier always QUOTES base+4 credits (set max_credits accordingly), and the 4-credit resolution surcharge is refunded at settlement when the domain was resolved before, so known domains settle at the base price. A domain that cannot be verified to a company returns INVALID_INPUT with the closest matches instead of a guessed company. Costs 4 credits base. Do not guess a slug from a brand name: slugs are vanity strings and a famous name can belong to an unrelated company's page (linkedin.com/company/anthropic is a small investment fund, not the AI lab). When you only know the company's name, pass its website domain instead -- the verified form -- and sanity-check the returned industry and description against what you expected. This tool does not search by name: if you only have an approximate company name, use linkedin_search_people's current_company filter with keywords (names are matched natively there) or give the exact slug. For the company's posts, use linkedin_get_posts with the same identifier (URL, slug, or website domain all work there too).

Input Schema

TableJSON Schema
NameRequiredDescriptionDefault
freshnessNorecent (default) serves cached data from the last few hours when available; realtime forces a live fetch for +2 credits (refunded if we fall back to cached data). Trial keys are cached-only and reject realtime with TRIAL_CAP_EXCEEDED; paying upgrades this same key to unlock it.recent
identifierYesCompany URL, slug (after /company/), or website domain (e.g. 'microsoft.com'). Numeric ids/URNs are not fetchable; use them only in linkedin_search_people company filters.
max_creditsNoSpend ceiling for this one call. The call is rejected (nothing charged) if its quote exceeds this. Only the quote is ever reserved, never this ceiling.

Output Schema

TableJSON Schema
NameRequiredDescriptionDefault
usageYes
commonYes
entityYes
platformYes
freshnessYes
data_as_ofYes
canonical_urlYes
schema_versionYes
platform_fieldsYes
Behavior5/5

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

Annotations declare readOnlyHint and openWorldHint. The description adds critical behavioral details: domain resolution surcharge and refund, error handling for unverified domains, trial key limitations on freshness, and the non-fetchability of numeric IDs. No contradictions.

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?

The description is informative but somewhat lengthy with multiple paragraphs. While every sentence adds value, a more streamlined structure could improve readability. Still well-organized with clear warnings and usage notes.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness5/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the tool's complexity (3 parameters, output schema exists), the description thoroughly covers all aspects: input validation, error states, credit costs, alternatives, and expected return data. No gaps remain.

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?

With 100% schema coverage, the description adds significant value: it explains valid identifier types (URL, slug, domain), warns against numeric IDs, clarifies freshness modes and trial restrictions, and describes max_credits behavior. This goes well beyond the schema.

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 the tool fetches a company's LinkedIn page and lists the fields returned. It distinguishes from siblings by mentioning linkedin_get_posts for posts and linkedin_search_people for name-based searches.

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?

The description provides explicit guidance on when to use this tool versus alternatives, including handling approximate names, slug guessing, and domain verification. It also explains credit costs and behaviors.

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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