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Contact Classifier

БесплатноПоддерживается

Classify a job title into department and seniority, and optionally verify the position.

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Описание

Classify a job title into department and seniority, and optionally verify the position.

README

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

Contact Classifier MCP server

MCP server for the Mamba Labs Contact Classifier actor on Apify.

Give it a job title and it returns the department and the seniority level. Give it a name and a company domain too, and it also checks whether that person is still listed on their employer's own website. The classification is a deterministic rule table, so it needs no API key and returns the same answer for the same title every time.

Install

npx -y @mambalabsdev/mcp-contact-classifier

Claude Desktop

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

Get an Apify token at console.apify.com/account/integrations.

Tool

classify_contact

One contact in, one classified row out. Only the job title is required.

Input Type Required Notes
job_title string yes The contact's job title, exactly as you hold it. Classified by deterministic rules with no API key needed.
full_name string no Only needed for position verification. Classification works without it. This name is never sent to any language model.
company_domain string no Only needed for position verification. The company's website domain, with or without https.
verify_position boolean no Check whether the person is still listed on their employer's own website. Adds roughly 3 seconds and 9 requests per contact, and needs both the name and the domain. Default false.
use_llm_fallback boolean no Sends titles the rules cannot place to your own model, using the LLM_API_KEY secret environment variable on your own copy of the actor. Only the title is sent, never the person's name. Default false.
llm_provider enum no openai, anthropic or google. Which provider your LLM_API_KEY belongs to. Only read when the LLM fallback is on. Default openai.
llm_model string no Model id passed straight through to the provider. Only read when the LLM fallback is on. Default gpt-4o-mini.
skipCache enum no Set to true to ignore cached results and classify from scratch. Default false.
max_charge_usd number no Optional cost cap in USD for this run. The actor stops before an item that could take its charges past this amount. Omit to rely on the maximum cost per run set in Apify.

Reading the output

21 flat snake_case fields, one row per contact. department is one of 12, seniority one of 12, and seniority_rank is 1 to 12 so a decision maker filter is a comparison rather than a list of strings. classification_rule names the rule that fired, which is what makes the decision auditable.

With no LLM_API_KEY set, titles the rules cannot place come back null. The row is still returned.

Billing

You are charged per contact classified, plus a small actor start fee. The deterministic classification calls no model and no third party API.

Event Price Fires when
apify-actor-start $0.00005 Once per run, on start, one event per GB of memory (minimum one). Apify's start event.
contact-classified $0.003 (FREE tier), down to $0.00255 on GOLD and above Once per contact, including a title the rules cannot place.

Full pricing is on the actor's Apify page. Running this server consumes Apify credits.

The tool starts the actor run and polls it to a finished status, so a run with verify_position on is not cut off at 300 seconds. A run that does not succeed comes back as an error with its run ID and status.

What this server does and does not do

It is a thin client for the Apify actor. It passes your input through and returns the actor's output unchanged. Every behavior described above lives in the actor, not here.

This actor does not discover people. The name and title come from you. It classifies a title and, optionally, checks a name against a page the employer published. To find people in the first place, use People Finder.

Errors are surfaced, never swallowed. An invalid input, an invalid token, an exhausted balance, a timeout, or a run that returns anything other than a dataset all come back as an explicit tool error rather than as an empty result.

Source

The actor is on the Apify Store. This wrapper is MIT licensed.

Built by Mamba Labs

from github.com/mambalabsdev/mcp-contact-classifier

Установить Contact Classifier в Claude Desktop, Claude Code, Cursor

Рекомендуется · одна команда, все IDE
unyly install mcp-contact-classifier

Ставит в Claude Desktop, Claude Code, Cursor и VS Code — сам разбирается с npx, uvx и сборкой из исходников.

Впервые? Поставь CLI: curl -fsSL https://unyly.org/install | sh

Или настроить вручную

Выполни в терминале:

claude mcp add mcp-contact-classifier --env APIFY_TOKEN="" -- npx -y @mambalabsdev/mcp-contact-classifier

Пошаговые гайды: как установить Contact Classifier

FAQ

Contact Classifier MCP бесплатный?

Да, Contact Classifier MCP бесплатный — установка в пару кликов через Unyly без оплаты.

Нужен ли API-ключ для Contact Classifier?

Да, требуются переменные окружения: APIFY_TOKEN. Unyly подставит их в конфиг при установке.

Contact Classifier — hosted или self-hosted?

Self-hosted: сервер запускается локально на твоей машине командой из раздела установки.

Как установить Contact Classifier в Claude Desktop, Claude Code или Cursor?

Открой Contact Classifier на unyly.org, выбери вкладку своего клиента (Claude Desktop, Claude Code, Cursor) и нажми Install — конфиг сгенерируется автоматически, без правки JSON.

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