Icp Fit Scorer
About
Scores any company against your ideal customer profile from a template, a weights config, or a plain-English description, returning a 0 to 100 score and tier. Part of the Mamba Labs signal toolkit.
Details
- Author
- mambalabsdev
- Downloads
- 345
- Categories
- Marketing, Other, Developer Tools
Jump to
- Flexible ICP input: template, JSON weights, or plain-English description
- 0 to 100 score with A/B/C tier classification
- Per-signal breakdown explaining what matched and what did not
- Flat JSON output ready for Clay column mapping
- Replaces manual formula columns with a single enrichment call
Setting up with Highlight
This MCP is not yet compatible with Highlight’s one-click setup. However, you can still use it with Highlight by following these steps:
- Download and install Highlight from highlightai.com/download
- Navigate to the plugins tab and select "Add Custom Plugin"
-
Configure the plugin with the settings below
Plugin Name
Icp Fit ScorerCommand (node, npx, python, etc.)Please refer to the README for specific instructions on how to obtain API keys or other required environment variables.
- Enable "Start Automatically" if you want the plugin to start when Highlight launches
From the repository
Pass a company domain along with your ICP definition (template, weights config, or plain-English description). The server evaluates the company against your criteria and returns a scored, tiered result with reasoning. Configure it with your Apify token in the server config.
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.
Claude Desktop / Cursor
Paste into your MCP client config file to install this server.
{
"mcpServers": {
"icp fit scorer": {
"mamba-icp": {
"command": "npx",
"args": [
"-y",
"@mambalabsdev/mcp-icp-fit-scorer"
],
"env": {
"APIFY_TOKEN": "your-apify-token"
}
}
}
}
}McpServers
{
"mamba-icp": {
"command": "npx",
"args": [
"-y",
"@mambalabsdev/mcp-icp-fit-scorer"
],
"env": {
"APIFY_TOKEN": "your-apify-token"
}
}
}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
- Quick start
- Prerequisites
- Example prompts
- Inputs
- Output
- Example output
- Features
- Full actor documentation
- Mamba Labs GTM Suite
- License
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 onfetch_signalsand 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.
You need Node.js 18 or newer and an Apify account with an API token.
{ "mcpServers": { "mamba-icp-scorer": { "command": "npx", "args": ["-y", "@mambalabsdev/mcp-icp-fit-scorer"], "env": { "APIFY_TOKEN": "your-apify-token" } } } }
Get your token athttps://console.apify.com/account/integrations, paste it in, and restart Claude Desktop. Thescore_icp_fittool will be available.
- Node.js 18 or newer
- An Apify account with an API token
- "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."
- 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. Requiresllm_api_key.
- llm_api_key(optional): your OpenAI or Anthropic key, used only withicp_description.
- llm_provider(optional):openaioranthropic.
- fetch_signals(optional): let the actor gather hiring and tech-stack signals automatically.
- include_explanation(optional): add ascore_explanationstring to the output.
Define your ICP with exactly one oftemplate,scoring_config, oricp_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.
The tool returns the actor's flat JSON row for the scored company, includingicp_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.
{ "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" }
- 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
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
This server is part of theMamba 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.https://apify.com/mambalabs
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