SiteCheck Company Records

Public LinkedIn company page facts: name, description

b2b_linkedin_company_profiles

Read-only

Public LinkedIn company page facts: name, description, headquarters address, employee count band, website links and logo, from the schema.org data LinkedIn publishes on the public company page (no login). Posts, followers and people are not included. Related tools on this server, for a different need: b2b_payscale_company_benefits when you need US employer pay and benefits from PayScale: most reported; b2b_seek_company_reviews when you need employer reviews from SEEK, the main job site of Australia. Price: $0.002 in USDC per successful call, paid with x402 (Base, Solana or Arc); failed calls are not charged.

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": {
      "company": "<company>"
    },
    "name": "b2b_linkedin_company_profiles"
  }
}

Input Schema

Name	Required	Description	Default
`company`	Yes	LinkedIn company slug (linkedin.com/company/<slug>) or the full company URL (required)

Output Schema

Name	Required	Description	Default
`url`	No
`logo`	No
`name`	No
`sameAs`	No
`source`	No
`address`	No
`company`	No
`checkedAt`	No
`employees`	No
`description`	No

Schema Changelog

Changes observed during successful MCP inspections.

First observedOct 23, 2026

TDQS

A4.4/5.0

Behavior5/5

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

Beyond readOnlyHint/openWorldHint, the description discloses that no authentication is required, that data comes from LinkedIn's published schema.org markup, which data classes are absent, and a precise cost model ($0.002 USDC per successful call via x402, failed calls not charged). For a paid, open-world lookup these are exactly the operational facts an agent needs before invoking.

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 definition is front-loaded with the returned facts and exclusions, then pricing, which is the right ordering, and uses consistent colon-delimited structure. It is somewhat long for a one-parameter tool because the two cross-sell sentences (PayScale, SEEK) are only loosely related to this resource and could be trimmed.

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?

With a one-parameter schema at full coverage, an output schema handling return values, and annotations covering the read-only/open-world profile, the description fills the remaining gaps: data source, no-login access, excluded data classes, and billing behavior. Nothing an agent needs in order to decide to call it or to call it correctly is missing.

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?

Schema description coverage is 100% and the single parameter is already documented as a LinkedIn slug or full company URL, so the schema carries the load. The description adds only indirect meaning (the target is a public company page), which does not go beyond what the schema states; the high-coverage baseline of 3 applies.

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 names the exact resource (public LinkedIn company page facts) and enumerates the returned fields: name, description, headquarters address, employee count band, website links and logo. It also states what is out of scope ('Posts, followers and people are not included') and the underlying data source, so an agent can distinguish it from review, benefits or traffic-estimate siblings without opening the schema.

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?

It gives a clear usage context ('from the schema.org data LinkedIn publishes on the public company page (no login)') and explicitly routes two adjacent needs away to named alternatives (b2b_payscale_company_benefits for pay/benefits, b2b_seek_company_reviews for reviews). It does not, however, say when *not* to use this tool in favour of the many other company-profile siblings (e.g. b2b_similarweb, b2b_manta, b2b_clutch_reviews), so the routing is partial.

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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