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mambalabsdev/mcp-gtm-tech-stack-signal-scraper

by Various

MCP server for GTM Tech Stack Signal Enrichment. Detects CRM, sequencer, and marketing automation tools from a company's public website via Apify. Clay-ready output.

MCP

mambalabsdev/mcp-gtm-tech-stack-signal-scraper

Added 23 Sept 2026

Overview

MCP server that identifies GTM tools such as CRMs, sequencers, and marketing automation platforms from a company's public website. Uses Apify for website scraping and outputs data formatted for Clay enrichment workflows. Written in TypeScript.

Best for

Best for
GTM builders using MCP clients who need quick tech stack signals for prospecting.

Use cases

  • Enrich lead records with detected GTM stack
  • Identify prospect tool usage from websites before outreach
  • Automate GTM tech stack detection in MCP-compatible workflows

How to use

Tested with

Claude Desktop, ChatGPT

Example client config

{\n  "mcpServers": {\n    "mamba-gtm-tech-stack": {\n      "command": "npx",\n      "args": ["-y", "@mambalabsdev/mcp-gtm-tech-stack-signal-scraper"],\n      "env": {\n        "APIFY_TOKEN": "your-apify-token"\n      }\n    }\n  }\n}

Notes

MCP server that identifies GTM tools such as CRMs, sequencers, and marketing automation platforms from a company’s public website. Uses Apify for website scraping and outputs data formatted for Clay enrichment workflows. Written in TypeScript.

1 stars on GitHub. Last updated 2026-09-22. Licensed MIT.

Use cases

  • Enrich lead records with detected GTM stack
  • Identify prospect tool usage from websites before outreach
  • Automate GTM tech stack detection in MCP-compatible workflows

Pros

  • Simple integration via MCP protocol
  • Output structured for Clay enrichment
  • Uses Apify for scalable scraping

Cons

  • Limited project maturity with few adoption signals
  • Detection accuracy depends on website visibility
  • No built-in database or caching layer

Indexed from awesome-mcp-servers-punkpeye and enriched against its public facts.

Pros

  • Simple integration via MCP protocol
  • Output structured for Clay enrichment
  • Uses Apify for scalable scraping

Cons

  • Limited project maturity with few adoption signals
  • Detection accuracy depends on website visibility
  • No built-in database or caching layer

Pairs with

Other entries in the index that connect to this one. Click through to see the chain.

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