PostHog MCP Server
Official PostHog MCP Server for product analytics, feature flags, experiments, and more.
AI lead qualification: ICP filter, 0-100 scoring, Hunter.io enrichment, HubSpot/Pipedrive export.
选择你使用的 AI 应用,然后复制设置。
通过 3/3 项检查 · 在你的电脑上运行 · ✓ 命令已检查 2026年11月13日
第一步:你用哪个 AI?
选择你用的应用,步骤会显示在下方。
可连接 Claude,但需要先准备
需要先在电脑上安装 Node.js(只需一次) · 仅限电脑上的 Claude 应用(Windows 或 Mac),网页和手机不可用
SettingsDeveloperEdit Config
{
"mcpServers": {
"leadpipe": {
"command": "npx",
"args": [
"-y",
"leadpipe-mcp-server"
]
}
}
}用 Leadpipe MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude 说正在使用 Leadpipe MCP,或回答中出现 Leadpipe MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
ChatGPT 暂时无法连接 Leadpipe MCP
Leadpipe MCP 需要安装在你的电脑上,而 ChatGPT 只能连接在网上运行的服务器
A · 使用能连接 Leadpipe MCP 的应用
B · 营销与社交媒体 类中能连接 ChatGPT 的其他服务器
从同类中使用较多的里挑选,仅限用地址即可连接、无需密钥的
可连接 Claude Code,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
Mac:按 Command+空格,输入 Terminal · Windows:打开 PowerShell
claude mcp add --scope user leadpipe -- npx -y leadpipe-mcp-server应能看到 Leadpipe MCP 在列表中并显示已连接
用 Leadpipe MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Claude Code 说正在使用 Leadpipe MCP,或回答中出现 Leadpipe MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Anthropic 帮助页面核实 · 你的界面可能因版本略有不同
可连接 Cursor,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
用 Leadpipe MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 Cursor 说正在使用 Leadpipe MCP,或回答中出现 Leadpipe MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 Cursor 帮助页面核实 · 你的界面可能因版本略有不同
可连接 VS Code,但需要先准备
需要先在电脑上安装 Node.js(只需一次)
用 Leadpipe MCP 告诉我你能帮我做什么,然后做一件给我看✓ 如果 VS Code 说正在使用 Leadpipe MCP,或回答中出现 Leadpipe MCP 的工具名称,就表示连接成功
菜单名称已于 2026年11月11日 对照 VS Code 帮助页面核实 · 你的界面可能因版本略有不同
Claude Code
claude mcp add --scope user leadpipe -- npx -y leadpipe-mcp-serverClaude Desktop (claude_desktop_config.json)
{
"mcpServers": {
"leadpipe": {
"command": "npx",
"args": [
"-y",
"leadpipe-mcp-server"
]
}
}
}Cursor (~/.cursor/mcp.json)
{
"mcpServers": {
"leadpipe": {
"command": "npx",
"args": [
"-y",
"leadpipe-mcp-server"
]
}
}
}VS Code (mcp.json)
{
"servers": {
"leadpipe": {
"type": "stdio",
"command": "npx",
"args": [
"-y",
"leadpipe-mcp-server"
]
}
}
}这些设置是根据作者在注册中心的条目生成的。如果 README 的写法不同,请以 README 为准。
依据这个服务器的连接方式和各应用自己的文档,检查于 2026年11月10日。
在你的电脑上运行,适用于
ClaudeGooseLM StudioMsty StudioCherry StudioCursorVS Code + GitHub CopilotVisual StudioWindsurf (Devin Desktop)ZedKiroClineContinueJetBrains AI AssistantClaude CodeCodex CLIGemini CLIOpenCodenodecodeWarpLibreChatRaycast
它在你的电脑上运行;安装后,工具列表会显示在你的 AI 应用中。
我们检查的是任何人都能看到的信息;我们没有阅读代码。不适用的检查项不计入总数。 · 检查于 2026年11月14日
注册名称已通过 GitHub 账户 automatiabcn 验证,该账户拥有此仓库
代码最后更新于2个月前
使用 MIT
它在你的电脑上运行,没有互联网端点
在你的电脑上运行,改用本地设置
它在你的电脑上运行;我们只能读取互联网端点的工具列表
提问、分享技巧或报告无法使用。按“有帮助”数量排序。
还没有评价
与注册中心里这个分类中所有活跃的服务器比较。只有当发布者拥有该仓库时,星标数才计入。
它在 3 项适用的基础检查中通过了 3 项。我们没有阅读代码。在添加密钥或令牌之前,请先看看这个服务器要求哪些权限,并使用只具备所需权限的密钥。
注册中心没有列出必需的密钥,不过有些服务器会在首次使用时要求你登录。
它在你的电脑上运行。你的 AI 应用会在需要时启动它,数据不会经过作者的服务器(除非它自己调用在线服务)。
GitHub 仓库最后更新于2个月前,最新版本(1.4.0)于7个月前进入注册中心。
支持 MCP 的应用,例如 Claude Desktop、Claude Code、Cursor 和 VS Code。你的电脑上还需要有 Node.js。
同一分类中有:PostHog MCP Server, Openshorts, Google Ads + Meta Ads + SEO/GEO MCP
同一分类中其他作者的服务器,按星标数排序。
如何看卡片:↓ 每周下载量i★ GitHub 星标i三大支柱:评价 · 检查 · 使用情况i更新 = 最新版本发布到注册中心的时间i
Official PostHog MCP Server for product analytics, feature flags, experiments, and more.
Turn long videos into viral vertical shorts and publish them to TikTok, Instagram and YouTube.
Google Ads, Meta Ads, Google Analytics (GA4) & Search Console MCP server by Ryze AI
按原文从作者的仓库复制。PopNx MCP 没有审查或背书其内容。HTML、图片和脚本已被移除。链接会离开本站。
AI-powered lead qualification engine for the Model Context Protocol
LeadPipe ingests leads from any source, enriches them with company data, scores them 0-100 using configurable AI rules, and exports qualified leads to your CRM — all through the MCP protocol.
git clone https://github.com/enzoemir1/leadpipe-mcp.git
cd leadpipe-mcp npm ci npm run build
Add to your MCP client config:
{
"mcpServers": { "leadpipe": { "command": "node", "args": ["path/to/leadpipe-mcp/dist/index.js"] } } }
Seed the pipeline with a realistic demo dataset — 14 leads across 5 archetypes (hot decision-makers, warm mid-level, cold junior/small-co, raw unenriched, and disqualified) — so every downstream tool returns meaningful output without any API keys. Safe to call multiple times; each call appends a fresh batch with new UUIDs.
{}
Returns: counts by status plus sample_lead_ids you can feed into lead_enrich, lead_score, or lead_export.
Filter leads against your Ideal Customer Profile before spending any enrichment credits. Uses only locally-available signals — email domain, job title, country, industry, company size, tech stack — so nothing is charged to Hunter.io, HubSpot, or Pipedrive.
{
"criteria": { "reject_freemail": true, "required_title_keywords": ["vp", "director", "head", "founder"], "target_countries": ["US", "CA", "GB"], "min_company_size": "11-50", "required_tech_stack": ["shopify"] }, "auto_disqualify": true }
Returns per-lead qualified/rejected decisions with reasons and an estimated credit savings figure.
Pairs well with platform detection tools. If you chain a tool like Detecto (
detect_platform) *before*lead_qualify, the detected tech stack populatescompany.tech_stack, andrequired_tech_stackcan drop wrong-platform leads before they ever reach enrichment or scoring.
Add a single lead to the pipeline.
{
"email": "jane@acme.com", "first_name": "Jane", "last_name": "Smith", "job_title": "VP of Engineering", "company_name": "Acme Corp", "company_domain": "acme.com", "source": "website_form", "tags": ["demo-request"] }
Add 1-100 leads at once. Duplicates are automatically skipped.
{
"leads": [ { "email": "lead1@corp.com", "job_title": "CEO" }, { "email": "lead2@startup.io", "job_title": "CTO" } ] }
Enrich a lead with company data using the email domain.
{ "lead_id": "uuid-of-lead" }
Returns: company name, industry, size, country, tech stack, LinkedIn URL.
Calculate a qualification score (0-100). Leads scoring 60+ are marked qualified.
{ "lead_id": "uuid-of-lead" }
Returns score + detailed breakdown across all 6 dimensions.
Search and filter leads with pagination.
{
"query": "acme", "status": "qualified", "min_score": 60, "limit": 20, "offset": 0 }
Export leads to CRM or file format.
{
"target": "hubspot", "min_score": 60 }
Targets: hubspot, pipedrive, csv, json
Google Sheets export is on the roadmap. Currently returns Sheets-ready formatted data.
Get pipeline analytics. No input required.
Returns: total leads, status/source breakdown, average score, score distribution, qualified rate, leads today/week/month.
View or update scoring configuration.
{
"job_title_weight": 0.30, "high_value_titles": ["ceo", "cto", "vp", "founder"], "custom_rules": [ { "field": "company_industry", "operator": "equals", "value": "fintech", "points": 15, "description": "Bonus for fintech companies" } ] }
| Resource | Description |
|---|---|
leads://recent | The 50 most recently added leads |
leads://pipeline | Pipeline summary with status counts, scores, conversion rates |
leads://config | Current scoring engine configuration |
Leads are scored 0-100 using a weighted average of 6 dimensions:
| Dimension | Default Weight | How It Works |
|---|---|---|
| Job Title | 25% | C-level/Founder: 100, VP/Director: 85, Manager: 65, Senior: 50, Junior: 15 |
| Company Size | 20% | Preferred sizes (11-50, 51-200, 201-500): 90, others scaled accordingly |
| Industry | 20% | High-value industries (SaaS, fintech, etc.): 90, others: 40 |
| Engagement | 15% | Phone provided, full name, tags, source type (landing page > CSV) |
| Recency | 10% | Today: 100, last week: 75, last month: 35, 3+ months: 5 |
| Custom Rules | 10% | User-defined rules with -50 to +50 points each |
Formula: score = sum(dimension_score * weight)
Leads with score >= 60 are qualified. Below 60 are disqualified.
Set the HUBSPOT_API_KEY environment variable with your HubSpot private app access token.
export HUBSPOT_API_KEY="pat-xxx-xxx"
Set the PIPEDRIVE_API_KEY environment variable.
export PIPEDRIVE_API_KEY="xxx"
No configuration needed. Export returns data directly.
LeadPipe extracts the domain from the lead's email and looks up company data:
HUNTER_API_KEY is set) — returns organization, industry, country, tech stack| Tier | Price | Tools | Features |
|---|---|---|---|
| Free | €0 | lead_demo_seed, lead_ingest, lead_batch_ingest, lead_search, lead_score, config_scoring | Ingest, manual scoring, ICP pre-qualification |
| Pro | €19 lifetime | + lead_qualify, lead_enrich, lead_export, pipeline_stats | AI scoring, Hunter.io enrichment, CRM export, pipeline analytics |
One-time €19 lifetime license (3 machines) — no subscription. See [Pro License](#pro-license) below to buy and activate.
npm run dev # Hot reload development
npm run build # Production build npm test # Run unit tests npm run inspect # Open MCP Inspector
LeadPipe ships in Free mode — lead_demo_seed, lead_ingest, lead_batch_ingest, lead_search, lead_score, and config_scoring are open. The following tools require a Pro license:
lead_qualify — ICP pre-filterlead_enrich — domain knowledge-base enrichmentlead_export — HubSpot / Pipedrive / Google Sheets / CSV / JSONpipeline_stats — portfolio analyticsBuy a Pro License (€19, lifetime, 3 machines): https://automatiabcn.lemonsqueezy.com/buy/360565a3-2577-45e2-93dd-1548a881f456
Or get the [Indie MCP Stack Bundle](https://automatiabcn.lemonsqueezy.com/buy/55e932fd-8319-47f0-8e95-0b86a29f2617) (€69, all 4 servers).
Then activate by setting the env var:
export LEMONSQUEEZY_LICENSE_KEY=YOUR-KEY-HERE
Or in your Claude Desktop / MCP client config:
{
"mcpServers": { "leadpipe-mcp": { "command": "npx", "args": ["-y", "leadpipe-mcp-server"], "env": { "LEMONSQUEEZY_LICENSE_KEY": "YOUR-KEY-HERE" } } } }
Validation is cached locally for 24 h, so the server is fully offline-capable after the first run.
MIT License. See LICENSE for details.
Built by Automatia BCN.