@bingeljell/lead-gen-mcp

@bingeljell/lead-gen-mcp

Enables AI-assisted B2B lead generation by discovering, extracting, scoring, and exporting company leads from any MCP-compatible agent.

Category
访问服务器

README

@bingeljell/lead-gen-mcp

MCP server for AI-assisted B2B lead generation. Discover, extract, score, and export company leads — from any MCP-compatible agent (Claude Desktop, Cursor, Windsurf, Codex, etc.).

Context

  • This started as a lead pipeline inside Alfred — a personal AI agent built for outbound sales work
  • Extracted into a standalone MCP server so anyone selling to small and mid-sized businesses can use it with their agent of choice
  • Works across agents — not Claude-specific
  • Still under active development. See docs/release_notes.md for what's in each release

Tools

Tool What it does
lead_discover Search for companies matching a query and rank them by ICP fit. No browser required.
lead_extract Deep-extract a single company's name, emails, industry, and size from a URL via headless browser.
lead_generate Full pipeline: search → rank → extract → filter → save to CSV.

Requirements

  • Node.js ≥ 20
  • A search provider — SearXNG (self-hosted), Brave API key, or nothing (falls back to DuckDuckGo automatically)
  • An LLM key (Anthropic or OpenAI) — optional but improves extraction quality

Setup

Option A — Use via npx (no clone needed)

The server runs on demand. Just configure your agent to call it with npx:

"command": "npx",
"args": ["-y", "@bingeljell/lead-gen-mcp"]

Option B — Clone and build locally

git clone https://github.com/bingeljell/lead-gen-mcp
cd lead-gen-mcp
npm install
npx playwright install chromium   # one-time ~130MB browser download
npm run build

Point your agent at the built file:

"command": "node",
"args": ["/absolute/path/to/lead-gen-mcp/dist/server.js"]

Agent configuration

Claude Desktop

Edit ~/Library/Application Support/Claude/claude_desktop_config.json:

{
  "mcpServers": {
    "lead-gen": {
      "command": "npx",
      "args": ["-y", "@bingeljell/lead-gen-mcp"],
      "env": {
        "SEARXNG_BASE_URL": "http://localhost:8888",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "LEAD_GEN_OUTPUT_DIR": "/Users/you/leads"
      }
    }
  }
}

Restart Claude Desktop. The three tools appear automatically.

Cursor / Windsurf

Same config format — add to your MCP settings JSON and restart the editor.

OpenAI Agents SDK (Codex)

from agents import Agent
from agents.mcp import MCPServerStdio

lead_gen = MCPServerStdio(
    command="npx",
    args=["-y", "@bingeljell/lead-gen-mcp"],
    env={
        "SEARXNG_BASE_URL": "http://localhost:8888",
        "ANTHROPIC_API_KEY": "sk-ant-...",
        "LEAD_GEN_OUTPUT_DIR": "./leads"
    }
)

agent = Agent(name="LeadResearcher", mcp_servers=[lead_gen])

Claude Code (CLI)

claude mcp add lead-gen \
  -e SEARXNG_BASE_URL=http://localhost:8888 \
  -e ANTHROPIC_API_KEY=sk-ant-... \
  -e LEAD_GEN_OUTPUT_DIR=/Users/you/leads \
  npx -y @bingeljell/lead-gen-mcp

Environment variables

# Search — set at least one (falls back to DuckDuckGo if none set)
SEARXNG_BASE_URL=http://localhost:8888   # self-hosted SearXNG
BRAVE_SEARCH_API_KEY=                    # https://api.search.brave.com

# LLM — optional, improves extraction accuracy
ANTHROPIC_API_KEY=
OPENAI_API_KEY=

# Output directory for CSVs
LEAD_GEN_OUTPUT_DIR=./leads

Copy .env.example for a template.


ICP profiles

The profile parameter tunes candidate ranking for different buyer types:

Profile Target
generic Any business
msp Managed service providers (IT/cybersecurity)
si Systems integrators / Microsoft partners
event_planner Event planning / wedding businesses

Example prompts

"Find 10 MSPs in Austin, Texas and save their contact emails."

"Search for small systems integrators in the UK, vertical msp_uk, max 5 leads."

"Discover event planning companies in Chicago — profile: event_planner."

License

MIT

推荐服务器

Baidu Map

Baidu Map

百度地图核心API现已全面兼容MCP协议,是国内首家兼容MCP协议的地图服务商。

官方
精选
JavaScript
Playwright MCP Server

Playwright MCP Server

一个模型上下文协议服务器,它使大型语言模型能够通过结构化的可访问性快照与网页进行交互,而无需视觉模型或屏幕截图。

官方
精选
TypeScript
Magic Component Platform (MCP)

Magic Component Platform (MCP)

一个由人工智能驱动的工具,可以从自然语言描述生成现代化的用户界面组件,并与流行的集成开发环境(IDE)集成,从而简化用户界面开发流程。

官方
精选
本地
TypeScript
Audiense Insights MCP Server

Audiense Insights MCP Server

通过模型上下文协议启用与 Audiense Insights 账户的交互,从而促进营销洞察和受众数据的提取和分析,包括人口统计信息、行为和影响者互动。

官方
精选
本地
TypeScript
VeyraX

VeyraX

一个单一的 MCP 工具,连接你所有喜爱的工具:Gmail、日历以及其他 40 多个工具。

官方
精选
本地
graphlit-mcp-server

graphlit-mcp-server

模型上下文协议 (MCP) 服务器实现了 MCP 客户端与 Graphlit 服务之间的集成。 除了网络爬取之外,还可以将任何内容(从 Slack 到 Gmail 再到播客订阅源)导入到 Graphlit 项目中,然后从 MCP 客户端检索相关内容。

官方
精选
TypeScript
Kagi MCP Server

Kagi MCP Server

一个 MCP 服务器,集成了 Kagi 搜索功能和 Claude AI,使 Claude 能够在回答需要最新信息的问题时执行实时网络搜索。

官方
精选
Python
e2b-mcp-server

e2b-mcp-server

使用 MCP 通过 e2b 运行代码。

官方
精选
Neon MCP Server

Neon MCP Server

用于与 Neon 管理 API 和数据库交互的 MCP 服务器

官方
精选
Exa MCP Server

Exa MCP Server

模型上下文协议(MCP)服务器允许像 Claude 这样的 AI 助手使用 Exa AI 搜索 API 进行网络搜索。这种设置允许 AI 模型以安全和受控的方式获取实时的网络信息。

官方
精选