Mcp Contact Classifier

com.mambabuilt由 mambabuilt.com 發布v1.2.1更新於 Nov 7, 2026

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

發布方自己的服務:classifier.dev

已驗證STDIO僅桌面資料與分析商業與電商

概覽

AI 產生的概覽

透過 Apify actor 將聯絡人的職稱歸類為部門與資歷等級,並可選擇核驗其職位是否仍列於雇主網站。

可以做什麼
  • 把職稱歸入 12 個部門與 12 個資歷等級之一,並給出 1 至 12 的 seniority_rank 與觸發的規則名稱
  • 在提供公司網域時,核驗某位具名人員是否仍列於其雇主網站
  • 可選擇把規則無法歸類的職稱交給自有的 LLM 供應商與模型處理
  • 每個聯絡人回傳一列 21 個扁平欄位,支援略過快取並設定單次執行費用上限
適用情境
當你已有帶職稱的聯絡人資料,需要一致且可稽核的部門與資歷標籤用於分群或篩選決策者,並可選擇確認此人是否仍列於公司網站時適用。
執行需求
透過 npx 以 stdio 在本機執行(需 Node.js)。需要 Apify 帳號與 APIFY_TOKEN 環境變數。可選的 LLM 備援需在自有 actor 副本上設定 LLM_API_KEY,並選擇供應商與模型。需要連線至 Apify 的網路。
安裝前請注意
依每個已分類聯絡人計費,另加 actor 啟動費,會消耗 Apify 額度;可用 max_charge_usd 限制單次執行費用。需要 APIFY_TOKEN 密鑰;啟用 LLM 備援時會把職稱傳送給第三方模型供應商(使用你的 LLM_API_KEY)。職位核驗會把姓名與公司網域傳送給 actor 進行網頁檢查。它不負責尋找人員。

安裝

在 SourceWeft 中

  1. 開啟 儀表板中的 Mcp Contact Classifier,將其新增到工作區。
  2. 為需要使用其工具的對話啟用該服務。

Desktop only,透過 STDIO。 STDIO 服務會啟動本機處理程序,因此需要 SourceWeft 桌面主機。

其他 MCP 客戶端

參照 儲存庫 中的啟動說明。

README

Contact Classifier MCP Server

[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

bash
npx -y @mambalabsdev/mcp-contact-classifier

Claude Desktop

json
{  "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.

InputTypeRequiredNotes
job_titlestringyesThe contact's job title, exactly as you hold it. Classified by deterministic rules with no API key needed.
full_namestringnoOnly needed for position verification. Classification works without it. This name is never sent to any language model.
company_domainstringnoOnly needed for position verification. The company's website domain, with or without https.
verify_positionbooleannoCheck 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_fallbackbooleannoSends 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_providerenumnoopenai, anthropic or google. Which provider your LLM_API_KEY belongs to. Only read when the LLM fallback is on. Default openai.
llm_modelstringnoModel id passed straight through to the provider. Only read when the LLM fallback is on. Default gpt-4o-mini.
skipCacheenumnoSet to true to ignore cached results and classify from scratch. Default false.
max_charge_usdnumbernoOptional 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.

EventPriceFires when
apify-actor-start$0.00005Once 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 aboveOnce 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

來源:README.md,提交 f7177bc

工具

1

classify_contact

Classify Contact

One contact in, one classified row out. Give it a job title and it returns the department, the seniority level, a seniority_rank from 1 to 12 you can filter with a comparison, and classification_rule, the named rule that fired, so every decision is auditable. The classification is a deterministic rule table: it needs no API key, calls no model, and returns the same answer for the same title every time. Only job_title is required. full_name and company_domain are read only when verify_position is on, which checks whether the person is still listed on their employer's own website and adds roughly 3 seconds and 9 requests per contact. The optional LLM fallback for titles the rules cannot place runs on your own key, set as the LLM_API_KEY secret environment variable on your own copy of the actor, and only the title is ever sent, never the person's name. With no key set those titles come back null rather than failing the row. This actor does not discover people: the name and title come from you. Requires an APIFY_TOKEN and consumes Apify credits. Reads public data only and writes nothing.

輸入參數full_namejob_title必填llm_modelskipCachellm_providercompany_domainmax_charge_usdverify_positionuse_llm_fallback

版本歷史

5
  1. v1.2.1最新Nov 5, 2026
  2. v1.2.0Oct 16, 2026
  3. v1.1.1Oct 6, 2026
  4. v1.1.0Oct 6, 2026
  5. v1.0.0Sep 16, 2026

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