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Scores a company against your ICP with weighted signals. Returns score, tier, and breakdown.
Scores a company against your ICP with weighted signals. Returns score, tier, and breakdown.
This is a well-structured MCP server that acts as a thin client to the Apify ICP Fit Scorer actor. Authentication is properly enforced via environment variable, permissions are narrowly scoped to HTTP network calls to Apify's API, and input validation is handled by Zod schemas. The server correctly rejects execution without a valid APIFY_TOKEN and handles error cases appropriately. Minor code quality improvements could be made around logging and error handling verbosity. Supply chain analysis found 3 known vulnerabilities in dependencies (0 critical, 3 high severity). Package verification found 1 issue.
3 files analyzed · 7 issues found
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Set these up before or after installing:
Environment variable: APIFY_TOKEN
Add this to your MCP configuration file:
{
"mcpServers": {
"com-mambabuilt-mcp-icp-fit-scorer": {
"env": {
"APIFY_TOKEN": "your-apify-token-here"
},
"args": [
"-y",
"@mambalabsdev/mcp-icp-fit-scorer"
],
"command": "npx"
}
}
}From the project's GitHub README.
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.
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.
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.
company_domain (required): the primary domain of the company to score. Example: clay.comcompany_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.
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.
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-fit-scorer
This is one of six actors in the Mamba Labs GTM Suite, covering hiring signals, tech stack detection, signal aggregation, job board keyword scanning, LinkedIn URL resolution, and ICP scoring. See them all at https://apify.com/mambalabs.
MIT
Built by Mamba Labs. https://apify.com/mambalabs
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