Mcp Contact Classifier

com.mambabuilt由 mambabuilt.com 发布v1.2.1更新于 Nov 7, 2026

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

发布方自己的服务:classifier.dev

已验证STDIO仅桌面数据与分析商业与电商

概览

AI 生成的概览

通过 Apify actor 将联系人的职位归类为部门和资历等级,并可选核验其职位是否仍列于雇主网站。

可以做什么
  • 把职位归入 12 个部门与 12 个资历等级之一,并给出 1 至 12 的 seniority_rank 和触发的规则名称
  • 在提供公司域名时,核验某位具名人员是否仍列于其雇主网站
  • 可选地把规则无法归类的职位交给自有的 LLM 提供商与模型处理
  • 每个联系人返回一行 21 个扁平字段,支持跳过缓存并设置单次运行费用上限
适用场景
当你已有带职位的联系人数据,需要一致且可审计的部门与资历标签用于分群或筛选决策者,并可选确认此人是否仍列于公司网站时适用。
运行要求
通过 npx 以 stdio 在本机运行(需 Node.js)。需要 Apify 账号和 APIFY_TOKEN 环境变量。可选的 LLM 回退需在自有 actor 副本上配置 LLM_API_KEY,并选择提供商与模型。需要访问 Apify 的网络连接。
安装前请注意
按每个已分类联系人计费,另加 actor 启动费,会消耗 Apify 额度;可用 max_charge_usd 限制单次运行费用。需要 APIFY_TOKEN 密钥;启用 LLM 回退时会把职位发送给第三方模型提供商(使用你的 LLM_API_KEY)。职位核验会把姓名与公司域名发送给 actor 进行网页检查。它不负责查找人员。

安装

在 SourceWeft 中

  1. 打开 控制台中的 Mcp Contact Classifier,将其添加到工作区。
  2. 为需要使用其工具的对话启用该服务。

Desktop only,通过 STDIO。 STDIO 服务会启动本地进程,因此需要 SourceWeft 桌面宿主。

其他 MCP 客户端

参照 仓库 中的启动说明。

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

来源: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.

输入参数full_namejob_title必填llm_modelskipCachellm_providercompany_domainmax_charge_usdverify_positionuse_llm_fallback

版本历史

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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