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mcp-icp-fit-scorer

by mambalabsdev

ICP Fit Scorer MCP Server

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

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's Inside

Related MCP server: Company Firmographic Enricher MCP Server

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.

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.

Actor

Immutable Actor ID

GTM Hiring Signal Scraper

D7O1SA2EqwHGsGr1P

GTM Tech Stack Signal Enrichment

qyd7nNyqFPelQViBx

GTM Signals Aggregator

xKdRfnfFNkdMpFuNs

Job Board Keyword Signal Scanner

4DvqpvhMR74NLcDDY

Domain to LinkedIn URL Resolver

3HtnSaqPHOg1Qg5gx

ICP Fit Scorer

W161DT8W4kW55dMFh

Domain Deliverability Checker

0tVgxI7A6o9jMlxmc

Company Firmographic Enricher

YlUtLWjfPpqykmB8g

Company Social Presence Mapper

4k6CCemkgBDz18m2h

Company Identity Resolver

lr8fTRAmZCBZmuwwh

Company Change-Event Feed

oX44rS0fkEJ3rXLWe

Funding & Press Signal Scanner

FS13X6dhQVgX3XOM6

Built by Mamba Labs | npm | Apify Store

License

MIT

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

Available Tools

1 tool
score_icp_fitScore ICP FitA
Read-onlyIdempotent

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.

ParametersJSON Schema
NameRequiredDescriptionDefault
company_domainYesThe primary domain of the company to score. Example: clay.com
company_nameNoOptional display name of the company.
templateNoName of a prebuilt scoring config. Alternative to scoring_config or icp_description.
scoring_configNoJSON object of scoring weights. Alternative to template or icp_description.
icp_descriptionNoPlain-English description of your target ICP. Requires llm_api_key. Alternative to template or scoring_config.
llm_api_keyNoYour OpenAI or Anthropic API key. Required only when using icp_description.
llm_providerNoLLM provider to use with icp_description: openai or anthropic.
fetch_signalsNoIf true, the actor fetches hiring and tech-stack signals for the company automatically before scoring.
include_explanationNoIf true, adds a score_explanation string to the output describing how the score was derived.

TDQS

A4.4/5.0
Behavior4/5

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

The description reveals key behavioral traits beyond annotations: read-only nature, requirement for APIFY_TOKEN, credit consumption, and output structure. It also clarifies the optional LLM usage for icp_description. This adds value on top of the readOnlyHint annotation. However, it does not discuss rate limits or error behavior.

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?

The description is concise, with two sentences. The first sentence states purpose and output; the second covers input methods and requirements. No wasted words, and key information is front-loaded.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

Given the complexity (9 parameters, no output schema), the description provides a good overview of inputs and outputs. It mentions the return format (icp_score, icp_tier, per-signal breakdown) and optional explanation. However, it omits details on the exact structure of the per-signal breakdown or scoring_config format, which are left to the schema. Overall, it is fairly complete.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is 100%, so baseline is 3. The description adds meaning by explaining the relationship between template, scoring_config, icp_description, and llm_api_key. It also clarifies the purpose of fetch_signals and include_explanation. This additional context improves parameter understanding.

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 clearly states the tool's purpose: scoring a company against an ICP using weighted signals. It specifies the output format (icp_score, icp_tier, per-signal breakdown) and input options. With no sibling tools, the purpose is unambiguous and distinct.

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 provides clear guidance on how to define the ICP (template, JSON, or plain-English description) and notes prerequisites (APIFY_TOKEN, credits). It also explains optional parameters like fetch_signals and include_explanation. While it doesn't explicitly state when not to use alternatives, the guidance is sufficient given no sibling tools.

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. Dates show when Glama detected each change.

  1. 1 tool updatev1.0.3
    • First observedscore_icp_fit

TDQS

A4.1/5.0
Disambiguation5/5

With only one tool, there is no risk of confusion between tools. The tool's purpose is clearly defined in its description.

Naming Consistency5/5

A single tool name cannot be inconsistent. The name 'score_icp_fit' follows a clear verb_noun pattern.

Tool Count2/5

A single tool feels insufficient for a server dedicated to ICP fit scoring. The domain likely requires additional tools for managing ICP templates, historical scores, or configuration.

Completeness2/5

The tool covers only the scoring operation. Missing are tools for creating or managing ICP templates, listing past scores, or configuring signals.

Maintenance

ActivityMaintained
ResponsivenessSyncing

Resources

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