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Lookalike Company Finder MCP Server

by mambalabsdev
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Lookalike Company Finder MCP Server

Smithery Glama score MCP Registry npm version npm downloads license mcpservers.org

An MCP server for the Mamba Labs Lookalike Company Finder actor on Apify. Give it seed company domains. Each run returns similar companies ranked by a similarity score, with the evidence behind each score.

What's Inside

Related MCP server: AI Tooling Detector MCP Server

What it does

Candidates come from the seed's "alternatives", "vs", and "competitors" search result pages and from the products list articles name, matched to their own sites. Each candidate homepage is read and compared with the seeds: shared distinctive words, search mentions, whether a comparison names it, and shared tools. Optional checks start Mamba Labs actors as child runs: Website Phrase and Business Model Classifier (on by default), B2B Reddit Conversation Monitor, and Integration and Marketplace Presence Mapper; Apify bills each to your account at its own prices. Review sites, list articles, and media are never returned.

All of the work runs on Apify. This package is a thin client that starts the actor run, waits for it, and hands back the dataset rows.

Quick start

You need Node.js 18 or newer and an Apify account with an API token.

Add this to your Claude Desktop config (claude_desktop_config.json), Cursor, or any MCP client:

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

Restart the client. The find_lookalike_companies tool appears.

Prerequisites

Example prompts

  • "Find 25 companies similar to smartlead.ai and instantly.ai, best match first."

  • "Which companies look like gong.io? Show why each one ranks where it does."

  • "Build a lookalike list from my three best customers, excluding mycompany.com."

Tool

find_lookalike_companies. One tool. Example arguments:

{
  "seed_domains": [
    "smartlead.ai"
  ],
  "max_results": "25"
}

Inputs

Every buyer facing input of the actor, unchanged. Omitted inputs take the actor's own defaults. Numbers and booleans are sent as strings.

Input

Type

Description

seed_domains

array

One to ten seed company domains

seed_domain

string

One seed domain

max_results

string

1 to 100. Default 25

min_score

string

0 to 100. Default 20

use_classifier

string

Business model check, child run. Default true

classify_top_n

string

1 to 50. Default 15

use_reddit

string

Reddit co-mentions, child run. Default false

use_marketplaces

string

Marketplace overlap, child run. Default false

exclude_domains

array

Domains never to return

max_charge_usd

string

Optional cost cap for the run

Pricing

Pay per event on Apify, billed to your Apify account. Prices in USD.

Event

Free plan

Bronze

Silver

Gold

lookalike-company, per company returned

$0.005

$0.00475

$0.0045

$0.00425

apify-actor-start, per run start

$0.0005

$0.0005

$0.0005

$0.0005

The seed summary row, no results rows, and error rows are free. The optional checks start Mamba Labs actors from your Apify account, billed at their own prices: Website Phrase and Business Model Classifier per domain classified, B2B Reddit Conversation Monitor per thread and summary row, Integration and Marketplace Presence Mapper per listing found.

Your keys

The actor needs no key. It reads search result pages and company homepages through the Apify proxy and holds no key of any kind. Mamba Labs pays the platform and proxy costs of this actor's own run; child runs are billed by their own actors.

Full actor documentation

apify.com/mambalabs/lookalike-company-finder

License

MIT. See LICENSE.

Available Tools

1 tool
find_lookalike_companiesFind lookalike companiesA
Read-only

Find companies similar to one to ten seed company domains (your best customers or a competitor). Returns lookalike companies ranked by a 0 to 100 similarity score with the evidence: score_breakdown_json (shared homepage words, search mentions, named in a comparison, shared tools, business model match, Reddit co-mention, shared marketplaces), found_via, shared_keywords, tech_detected, has_pricing_page, and homepage title, plus one free seed summary row. Candidates come from alternatives, vs, and competitors search result pages and the products list articles name; review sites, list articles, and media are never returned. use_classifier (default true), use_reddit, and use_marketplaces start Mamba Labs actors as child runs that Apify bills to your account at their own prices. A run that does not succeed is returned as an error with its run ID. Read-only; requires an APIFY_TOKEN; Apify charges per lookalike company returned.

NameRequiredDescriptionDefault
min_scoreNoOnly companies scoring at least this, 0 to 100. Default 20.
use_redditNoRun Mamba Labs B2B Reddit Conversation Monitor on each seed for co-mentions (child run billed at its own prices). Default false.
max_resultsNoLookalike companies returned, best first, 1 to 100. Each one is billed. Default 25.
seed_domainNoOne seed domain. Used together with seed_domains.
seed_domainsNoOne to ten seed company domains, for example smartlead.ai.
classify_top_nNoTop candidates the business model check reads, 1 to 50. Default 15.
max_charge_usdNoOptional. The run stops before any row that would take its charges over this amount. Child runs are billed by their own actors.
use_classifierNoRun Mamba Labs Website Phrase and Business Model Classifier on the seeds and top candidates (child run billed at its own price). Default true.
exclude_domainsNoDomains never to return, for example your own and your current customers.
use_marketplacesNoRun Mamba Labs Integration and Marketplace Presence Mapper on the seeds and top candidates (child run billed at its own price). Default false.

TDQS

A4.4/5.0
Behavior5/5

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

Beyond the annotations, the description discloses billing behavior for child runs, required APIFY_TOKEN authentication, per-lookalike charges, the error shape for failed runs, and the source boundaries for returned candidates. This is rich behavioral context for a read-only but expensive open-world tool.

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 front-loaded with purpose, ranking, and return evidence, then covers sources and billing. It is dense but appropriately sized for a complex tool with ten parameters and no output schema, though the long enumerations could be slightly more structured.

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, the description compensates by listing returned fields and the seed summary row, and with annotations present it still adds authentication, billing, error handling, and source exclusion details. An agent has enough context to call the tool correctly and understand its cost and output profile.

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%, so the schema already documents all ten parameters in detail. The description reinforces that use_classifier defaults true and that use_reddit/use_marketplaces start billed child runs, but most parameter meaning remains in the schema rather than added by the description.

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 states a specific verb and resource: find companies similar to one to ten seed domains. It also defines the ranking and evidence returned, so an agent can understand exactly what the tool produces 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 by naming seed domains as your best customers or a competitor, and it clarifies that candidates come from alternatives/vs/competitor pages and product-list articles while review sites, list articles, and media are never returned. There are no sibling tools to route against, so explicit alternative selection is not possible, but the use context is clear.

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 observedfind_lookalike_companies
Deploy Server
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

TDQS

A4.5/5.0

Scored across 1 tool

Disambiguation5/5

There is only one tool, so there is no possibility of confusing it with another operation. Its purpose—finding lookalike companies from seed domains—is clearly stated and distinct.

Naming Consistency5/5

The single tool name follows a clear verb_noun pattern: find_lookalike_companies. With only one tool, there is no inconsistency across the set.

Tool Count5/5

The server has a narrow, well-defined purpose, and the single tool directly implements that purpose. No additional tools are needed for the stated function, and the rich parameterization is contained within one coherent operation.

Completeness4/5

The tool covers the core lookalike-company discovery workflow, including seed handling, scoring, evidence breakdown, and source controls. Minor gaps exist around run status/polling or retrieving historical results, but the primary advertised capability is fully represented.

Maintenance

ActivityMaintained
ResponsivenessNo issues

Resources

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