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LinkedIn Company Page Mapper MCP Server

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TypeScript
Remote

LinkedIn Company Page Mapper MCP Server

Smithery Glama score npm version npm downloads license

MCP server for the Mamba Labs LinkedIn Company Page Mapper actor on Apify.

Resolve a company domain to its LinkedIn page with the exact follower count and public firmographics.

What it does

Resolve a company domain to its LinkedIn company page and return the EXACT follower count, plus the industry, declared company size band, headquarters, founded year and specialties that LinkedIn publishes on the public page. LinkedIn renders every digit, so unlike most social platforms these counts can be summed across a list. Everything comes from the logged out page: no login, no session cookie, no vendor. Employee lists, employee growth and post engagement are NOT reachable logged out and are not returned. A guessed slug that resolves to a different company is reported as identity_mismatch. Read only; requires an APIFY_TOKEN and consumes Apify credits per call.

Related MCP server: Company Firmographic Enricher MCP Server

Quick start

Add this to your MCP client configuration:

{
  "mcpServers": {
    "mamba-linkedin-company-presence-mapper": {
      "command": "npx",
      "args": ["-y", "@mambalabsdev/mcp-linkedin-company-presence-mapper"],
      "env": { "APIFY_TOKEN": "your-apify-token" }
    }
  }
}

Prerequisites

The actor is pay per event and consumes Apify credits per call. Pricing is on the actor page.

Example prompts

  • "How many LinkedIn followers does gitlab.com have?"

  • "Get the LinkedIn industry, size band and headquarters for stripe.com."

  • "Rank these domains by LinkedIn follower count: notion.com, figma.com, gitlab.com."

Tool and inputs

Tool: map_linkedin_company_presence

Input

Type

Meaning

company_domain

string

Bare company domain, for example shopify.com. Supply this or a handle. With a domain the actor runs full discovery; with a handle it skips straight to

company_name

string

Optional. Improves search accuracy and is what the identity gate checks a discovered profile against, so supplying it reduces wrong matches.

handle

string

Optional. The company slug from linkedin.com/company/, for example shopify. Supplying it skips discovery and goes straight to the fetch.

includeFollowerCounts

boolean

When "true" (default) the profile page is fetched and the counts are extracted. Set "false" to resolve the profile URL only, which is cheaper and need

skipCache

boolean

When "false" (default) a successful lookup is cached for seven days and reused. Set "true" to force a fresh fetch. Sent as a string for Clay compatibi

includeFirmographics

boolean

When "true" (default) industry, company size band, headquarters, founded year and website are parsed off the page alongside the follower count. Set "f

Reading the output

Every row carries a per platform _status field, and it is the field to read first. The vocabulary is the same across the whole Mamba Labs social family:

Status

Meaning

ok

fetched and parsed, the value is there

not_found

we looked and there is no such profile

not_extractable

the profile exists and the value is not on the wire to us

blocked

the platform refused us, worth retrying later

identity_mismatch

we found a real profile and it belongs to someone else

skipped

you did not ask for this platform

false and null are never interchangeable. false means we looked and the answer is no. null means we could not look. If you filter for companies with no presence, filter on false, because null rows are unknown rather than absent.

Full actor documentation

apify.com/mambalabs/linkedin-company-presence-mapper

Mamba Labs GTM Suite

Mamba Labs builds a fleet of GTM enrichment actors that share one flat, Clay ready output convention, so their rows join on company_domain with no cleaning step. Full fleet: apify.com/mambalabs

License

MIT

Available Tools

1 tool
map_linkedin_company_presenceMap LinkedIn Company Page PresenceA
Read-onlyIdempotent

Resolve a company domain to its LinkedIn company page and return the EXACT follower count, plus the industry, declared company size band, headquarters, founded year and specialties that LinkedIn publishes on the public page. LinkedIn renders every digit, so unlike most social platforms these counts can be summed across a list. Everything comes from the logged out page: no login, no session cookie, no vendor. Employee lists, employee growth and post engagement are NOT reachable logged out and are not returned. A guessed slug that resolves to a different company is reported as identity_mismatch. Read only; requires an APIFY_TOKEN and consumes Apify credits per call.

ParametersJSON Schema
NameRequiredDescriptionDefault
handleNoOptional. The company slug from linkedin.com/company/<slug>, for example shopify. Supplying it skips discovery and goes straight to the fetch.
skipCacheNoWhen "false" (default) a successful lookup is cached for seven days and reused. Set "true" to force a fresh fetch. Sent as a string for Clay compatibility.
company_nameNoOptional. Improves search accuracy and is what the identity gate checks a discovered profile against, so supplying it reduces wrong matches.
company_domainNoBare company domain, for example shopify.com. Supply this or a handle. With a domain the actor runs full discovery; with a handle it skips straight to the fetch.
includeFirmographicsNoWhen "true" (default) industry, company size band, headquarters, founded year and website are parsed off the page alongside the follower count. Set "false" for the URL and follower count only. Sent as a string for Clay compatibility.
includeFollowerCountsNoWhen "true" (default) the profile page is fetched and the counts are extracted. Set "false" to resolve the profile URL only, which is cheaper and needs no proxy. Sent as a string for Clay compatibility.

TDQS

A4.5/5.0
Behavior5/5

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

Annotations already mark readOnly, openWorld, idempotent, non-destructive. The description goes well beyond by disclosing logged-out access, exact digit rendering, non-reachability of certain data, identity_mismatch handling for guessed slugs, and the cost/credit implications. None of this contradicts the annotations; it enriches them.

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?

Six sentences, all substantive: purpose, output list, a distinguishing fact (summable counts), access mode, exclusions, and an identity edge case. Every sentence carries information an agent needs; there is no filler or redundant restating of annotations.

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 no output schema and zero required parameters, the description carries full responsibility for usability. It covers what is returned, what is not, how mismatches are reported, auth requirements, and cost implications. An agent has enough to decide and invoke correctly without guessing.

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 each parameter already has detailed descriptions, so the baseline is 3. The tool description adds some context around discovery vs. direct fetch and the Clay string conversion, but these are largely echoed in the schema. It does not introduce new parameter-level semantics beyond what the schema already conveys.

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 opens with a precise verb-resource pair ('Resolve a company domain to its LinkedIn company page') and enumerates the exact outputs: follower count, industry, size band, HQ, founded year, specialties. It clearly differentiates from typical social-platform tools by noting counts are exact and summable, leaving no ambiguity about the function.

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?

The description clearly states what the tool returns and, importantly, what it does NOT return (employee lists, growth, engagement) and that no login or session is needed. It also mentions the APIFY_TOKEN requirement and credit consumption. While it doesn't name a specific alternative tool, there are no siblings, and the conditions for choosing this approach (public logged-out data) are explicit enough.

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

Tool Schema Changelog

Recent tool additions, removals, and schema changes observed during successful MCP inspections.

  1. 1 tool updatev1.0.0
    • First observedmap_linkedin_company_presence
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TDQS

A4.5/5.0

Scored across 1 tool

Disambiguation5/5

With exactly one tool, no selection ambiguity exists. The tool's name and description clearly define it as a single-purpose LinkedIn company page lookup.

Naming Consistency5/5

The single tool uses descriptive snake_case with a clear verb-object pattern that matches the server's stated purpose, so the naming is coherent and unsurprising.

Tool Count4/5

One tool is slightly narrower than a typical MCP server, but it is substantive and fully aligned with the server's declared purpose. The count feels minimal rather than bloated or trivial.

Completeness4/5

The tool covers the logged-out LinkedIn page mapping surface well, returning the key company fields and explicitly documenting unavailable data types. It is not fully complete because there is no batch or multi-domain mapping capability.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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