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linkedin_company

Fetch a company's LinkedIn profile by ID to get its name, industry, size, employee and follower counts, headquarters, website, and recent updates.

Instructions

Get LinkedIn Company info by ID. Returns a LinkedIn company page by its company id. The response has the name, headline, about text, industry, company size, employee count on LinkedIn, follower count, founding year, headquarters, locations, specialties, website, logo, Products section cards, affiliated and similar pages, and recent updates with reactions and comment counts.

Input Schema

NameRequiredDescriptionDefault
idYesLinkedIn Company ID

Schema Changelog

Changes observed during successful MCP inspections.

  1. First observedv1.0.0

TDQS

B3.2/5.0
Behavior3/5

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

With no annotations and no output schema, the description carries the full burden, and it does enumerate the returned fields in detail (industry, employee count, followers, locations, updates, etc.), which is genuinely useful. However it says nothing about authentication requirements, rate limits, or error behavior for a missing id, so it is only partially transparent.

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?

Purpose is front-loaded in the first sentence, with the return-field inventory following. The field list is long but earns its place since there is no output schema; a slightly tighter grouping would be ideal.

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?

For a one-parameter read tool with no output schema, the description covers both the operation and the shape of the response thoroughly enough to call it correctly. The remaining gap is the absence of any access or error context, which is minor for a read.

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?

There is a single parameter with 100% schema description coverage, so the schema already documents `id` as the LinkedIn Company ID. The description adds no syntax, format, or sourcing detail (e.g. where to obtain the id), so the baseline 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?

States a specific verb and resource ("Get LinkedIn Company info by ID") and immediately clarifies the key is a company `id`. It is clearly a company-detail fetch, but it does not explicitly distinguish itself from siblings like linkedin_showcase or linkedin_products_search, so it stops short of a 5.

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 when-to-use guidance, no prerequisites, and no mention of alternative tools for related lookups (e.g. linkedin_products_search for finding companies). The only implied usage is the required `id`, leaving the agent to infer when this tool applies.

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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