ICP Fit Scorer MCP Server

An MCP server that scores a company against your ideal customer profile. It wraps the Mamba Labs ICP Fit Scorer actor on Apify and returns a Clay-ready flat JSON row to any MCP client.

What it does

Give it a company domain and a definition of your ICP, and it scores the company on weighted signals, returning a 0 to 100 score, an A to D tier, and a per-signal breakdown. Define your ICP three ways: a prebuilt template, a JSON scoring config, or a plain-English description (which uses your own LLM key). Turn on fetch_signals and the actor will gather hiring and tech-stack signals for you before scoring. One flat row, ready for Clay, a CRM, or an AI agent workflow. All of the scoring runs on Apify. This package is a thin client that calls the actor and hands back the result.

Related MCP server: mcp-gtm-tech-stack-signal-scraper

Quick start

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

Add this to your Claude Desktop config:

{
  "mcpServers": {
    "mamba-icp-scorer": {
      "command": "npx",
      "args": ["-y", "@mambalabsdev/mcp-icp-fit-scorer"],
      "env": {
        "APIFY_TOKEN": "your-apify-token"
      }
    }
  }
}

Get your token at https://console.apify.com/account/integrations, paste it in, and restart Claude Desktop. The score_icp_fit tool will be available.

Prerequisites

• Node.js 18 or newer

• An Apify account with an API token

Example prompts

• "Score clay.com against the b2b_saas template and fetch its signals."

• "How well does stripe.com fit an ICP of mid-market fintech companies? Explain the score."

• "Score figma.com with my scoring config and include the per-signal breakdown."

• "Rate openai.com against this ICP description: enterprise AI teams hiring for go-to-market."

Inputs

• company_domain (required): the primary domain of the company to score. Example: clay.com

• company_name (optional): display name of the company.

• template (optional): name of a prebuilt scoring config.

• scoring_config (optional): a JSON object of scoring weights.

• icp_description (optional): plain-English ICP description. Requires llm_api_key.

• llm_api_key (optional): your OpenAI or Anthropic key, used only with icp_description.

• llm_provider (optional): openai or anthropic.

• fetch_signals (optional): let the actor gather hiring and tech-stack signals automatically.

• include_explanation (optional): add a score_explanation string to the output.

Define your ICP with exactly one of template, scoring_config, or icp_description.

This server exposes the single-company scoring path. The actor also supports batch inputs (a dataset or CSV of companies) and a results webhook. For those, run the actor directly on Apify.

Output

The tool returns the actor's flat JSON row for the scored company, including icp_score (0 to 100), icp_tier (A to D), the per-signal breakdown, and an optional explanation. See the Apify Store page for the full output schema.

Example output

{
  "company_domain": "ramp.com",
  "icp_score": 87,
  "icp_tier": "A",
  "lead_tag": "priority",
  "score_hiring": 25,
  "score_tech_stack": 22,
  "score_headcount": 20,
  "score_funding": 20,
  "score_industry": 0,
  "run_date": "2026-05-28"
}

Features

• User-defined JSON scoring config with custom weights

• Returns icp_score (0 to 100), icp_tier (A to D), and lead_tag

• Per-signal point breakdown: hiring, tech stack, headcount, funding, industry

• Replaces 6+ manual formula columns in Clay

Full actor documentation

This server is a thin client and holds no scoring logic. For the complete input and output reference, pricing, and run history, see the Apify Store page:

https://apify.com/mambalabs/icp-account-lead-scoring-fit-scorer-0-100-for-clay

Mamba Labs GTM Suite

This server is part of the Mamba Labs GTM Suite, a fleet of twelve specialized MCP servers for go-to-market signal intelligence, each backed by a dedicated Apify actor.

Built by Mamba Labs | npm | Apify Store

License

MIT

Built by Mamba Labs. https://apify.com/mambalabs