Name: ICP Fit Scorer
Author: mambalabsdev

LABSCO SUMMARY

The output format is the product: a flat, Clay-ready row is what lets this sit inside an enrichment pipeline instead of beside one.

Scores arrive as columns rather than prose, which is what makes them sortable, thresholdable and comparable across a list. The ways of defining the profile are not equivalent in what they cost you: a template or a scoring_config runs on the Apify token alone, while an icp_description in plain English routes through a model and needs an llm_api_key of your own. Worth knowing before it is wired into a loop: this is read-only and consumes Apify credits on every call, so the cost scales with the list rather than with the profile.

What it is

A single-tool scorer that takes a company, applies weighted ICP signals to it, and returns a 0 to 100 icp_score, an A to D icp_tier and a per-signal breakdown as a flat, Clay-ready JSON row.

What you get

The profile stated whichever way suits: a prebuilt template, an explicit JSON scoring_config with your own weights, or a plain-English icp_description.
Output shaped for a spreadsheet rather than a paragraph: score_icp_fit returns a flat JSON row, so the score, the tier and each signal land as columns.
The reasoning available on request: include_explanation adds why the score came out where it did, and fetch_signals controls whether signals are gathered for the company at call time.
A read-only call: score_icp_fit reads about the company and scores it, and writes nothing anywhere.

Requirements

An Apify token, supplied as APIFY_TOKEN — each call spends Apify credits. Scoring from a plain-English icp_description additionally needs an llm_api_key for the chosen llm_provider; a template or a JSON scoring_config does not.

Setup effort

One command plus a key — npx -y @mambalabsdev/mcp-icp-fit-scorer, then supply credentials

See installation options

USE CASES

01

Ranking an inbound list before anyone calls it

Score each company against the profile you already use, and sort on the tier and the number that come back.

score_icp_fit

02

Turning a written ICP into a rule you can rerun

Describe the profile in plain English or set the weights explicitly in a scoring_config, then apply the same definition to every company that follows.

score_icp_fit

1 tool

Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.

`score_icp_fit`

Score a company against your ideal customer profile (ICP) using weighted signals. Returns a 0 to 100 icp_score, an A to D icp_tier, and a per-signal breakdown as a flat, Clay-ready JSON row. Define your ICP with a prebuilt template, a JSON scoring_config, or a plain-English icp_description (which requires llm_api_key). Read-only; requires an APIFY_TOKEN and consumes Apify credits per call.

BEFORE YOU INSTALL

What to know first

Tools you get

1 tool when we connected. Scoring many companies from a dataset or CSV and the results webhook require running the actor directly on Apify. Define the ICP with exactly one of template, scoring_config or icp_description; only icp_description needs your own model key.

Requires

npx on PATH
Node.js 18 or later
APIFY_TOKEN in your environment

Where it runs

The documented client runs the server on your machine.

CONTEXT COST

Light Standing cost

Rated by the size of the one tool definition that rides along on every turn

INSTALLATION

Connect your client

Server setup

TERMINAL

npx -y @mambalabsdev/mcp-icp-fit-scorer

MCP Clients

Claude Desktop

CLAUDE DESKTOP · JSON

{
  "mcpServers": {
    "mamba-icp-scorer": {
      "command": "npx",
      "args": [
        "-y",
        "@mambalabsdev/mcp-icp-fit-scorer"
      ],
      "env": {
        "APIFY_TOKEN": "<YOUR_APIFY_TOKEN>"
      }
    }
  }
}

ICP Fit Scorer