mcp-gtm-tech-stack-signal-scraper
This server detects which go-to-market (GTM) tools a company uses by analyzing its public-facing website, returning structured, Clay-ready JSON data.
Detect the GTM tech stack for any company by providing its domain (e.g.,
stripe.com), identifying tools across CRM, sequencer, and marketing automation categories.Get per-tool boolean flags for HubSpot, Salesforce, Apollo, Gong, Intercom, and Marketo.
Identify specific tool categories via dedicated fields:
crm_detected,seq_tool_detected, andmarketing_automation_detected.Get a composite tech stack signal (e.g.,
high,medium,low) summarizing overall GTM tool adoption, plus a total GTM tool count.Optionally crawl additional pages (pricing, product) for improved detection coverage.
Output flat JSON with all fields present in every row, ready to pipe into Clay, a CRM, or an AI agent workflow.
Detects whether a company uses HubSpot (CRM, marketing automation, or sequencer) by analyzing its public website scripts and pages.
Detects whether a company uses Intercom as a messaging tool by analyzing its public website.
Detects whether a company uses Salesforce as its CRM by analyzing its public website.
GTM Tech Stack Signal Enrichment MCP Server
An MCP server that detects which go-to-market tools a company runs, straight from its public website. It wraps the Mamba Labs GTM Tech Stack Signal Enrichment actor on Apify and returns a Clay-ready flat JSON row to any MCP client.
What's Inside
Related MCP server: lead-enrichment-mcp
What it does
Give it a company domain and it inspects the public-facing scripts and pages to detect the CRM, sequencer, and marketing automation tools in use. You get back per-tool boolean flags (HubSpot, Salesforce, Apollo, Gong, Intercom, Marketo), counts, and a composite tech stack signal, ready to drop into Clay, a CRM, or an AI agent workflow. All of the detection runs on Apify. This package is a thin client that calls the actor and hands back the result.
Quick start
You need Node.js 18 or newer and an Apify account with an API token.
Add this to your Claude Desktop config:
{
"mcpServers": {
"mamba-gtm-tech-stack": {
"command": "npx",
"args": ["-y", "@mambalabsdev/mcp-gtm-tech-stack-signal-scraper"],
"env": {
"APIFY_TOKEN": "your-apify-token"
}
}
}
}Get your token at https://console.apify.com/account/integrations, paste it in, and restart Claude Desktop. The detect_gtm_tech_stack tool will be available.
Prerequisites
Node.js 18 or newer
An Apify account with an API token
Example prompts
"What GTM tools does stripe.com run? Check their tech stack."
"Does openai.com use HubSpot or Salesforce? Detect their CRM."
"Pull the marketing automation and sequencer signals for figma.com."
"Detect the GTM tech stack for datadoghq.com and list every tool found."
Inputs
domain(required): the bare company domain, nohttps://and no trailing slash. Example:stripe.comcrawl_additional_pages(optional): if true, crawls up to 2 extra pages (pricing, product) for better coverage. Defaults to true when omitted.technologies(optional): report only these tools instead of every detectable one, which is how you answer "is this company using X". Selectable values:hubspot,salesforce,marketo,pardot,intercom,drift,apollo,outreach,gong,zoominfo. Omit for every tool.
Output
The tool returns the actor's flat JSON row for the scanned company. Fields include the detected CRM, sequencer, and marketing automation tools, a GTM tool count, a composite tech stack signal, and per-tool boolean flags such as uses_hubspot, uses_salesforce, uses_apollo, uses_gong, uses_intercom, and uses_marketo. See the Apify Store page for the full output schema.
Example output
{
"company_domain": "hubspot.com",
"crm_detected": "hubspot",
"seq_tool_detected": null,
"uses_hubspot": true,
"uses_salesforce": false,
"uses_apollo": false,
"uses_gong": false,
"uses_intercom": true,
"uses_marketo": false,
"marketing_automation_detected": "hubspot",
"gtm_tool_count": 2,
"tech_stack_signal": "high"
}Features
Per-tool boolean flags: HubSpot, Salesforce, Apollo, Gong, Intercom, Marketo
CRM and sequencer classification, plus marketing automation detection
Composite tech_stack_signal and gtm_tool_count
Flat JSON, every field present in every row
Full actor documentation
This server is a thin client and holds no detection logic. For the complete input and output reference, pricing, and run history, see the Apify Store page:
https://apify.com/mambalabs/gtm-tech-stack-signal-scraper
Mamba Labs GTM Suite
This server is part of the Mamba Labs GTM Suite, a fleet of twelve specialized MCP servers for go-to-market signal intelligence, each backed by a dedicated Apify actor.
Actor | Immutable Actor ID |
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Built by Mamba Labs | npm | Apify Store
License
MIT
Built by Mamba Labs. https://apify.com/mambalabs
Available Tools
1 tooldetect_gtm_tech_stackDetect GTM Tech StackARead-onlyIdempotent
Detect which GTM tools a company uses from its public-facing website. Returns CRM, sequencer, and marketing automation signals as a flat, Clay-ready JSON row, with per-tool boolean flags for HubSpot, Salesforce, Apollo, Gong, Intercom, and Marketo, plus a composite tech stack signal. Read-only; requires an APIFY_TOKEN and consumes Apify credits per call.
| Name | Required | Description | Default |
|---|---|---|---|
| domain | Yes | Bare company domain without https:// and without a trailing slash. Example: stripe.com | |
| crawl_additional_pages | No | If true, crawls up to 2 additional pages per domain (pricing, product) to improve detection coverage. Slightly increases run time. Defaults to true when omitted. |
TDQS
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already declare readOnlyHint=true, destructiveHint=false, idempotentHint=true. The description adds non-obvious details: requires APIFY_TOKEN and consumes credits. No contradiction. The description complements the annotations well.
Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.
Is the description appropriately sized, front-loaded, and free of redundancy?
The description is two sentences, front-loading the purpose and output, then adding requirements. Every sentence is informative. No wasted words.
Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.
Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?
Given no output schema, the description explains the return format. It covers prerequisites (APIFY_TOKEN), cost (credits), and the two parameters are well-documented in the schema. No missing context for effective use.
Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.
Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?
Schema coverage is 100% and both parameters have descriptions. The description does not add additional meaning beyond the schema, but it provides context about the output which indirectly relates to the domain parameter. Baseline 3 is appropriate.
Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.
Does the description clearly state what the tool does and how it differs from similar tools?
The description clearly states the tool's purpose: detecting GTM tools from a company's website. It specifies the output as a flat, Clay-ready JSON row with per-tool boolean flags and a composite signal. This is a specific verb+resource, and with no sibling tools, differentiation is not needed.
Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.
Does the description explain when to use this tool, when not to, or what alternatives exist?
The description provides clear usage context: it is read-only, requires an APIFY_TOKEN, and consumes Apify credits per call. It also implies this tool is for initial discovery. Since there are no sibling tools, explicit exclusion of alternatives is not required.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Tool Schema Changelog
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
1 tool update
v0.1.0- First observed
detect_gtm_tech_stack
TDQS
Only one tool exists, so there is no possibility of ambiguity or confusion between tools.
The single tool name 'detect_gtm_tech_stack' follows a clear verb_noun pattern, making it consistent in isolation.
One tool is perfectly appropriate for this narrow, well-defined domain of detecting GTM tech stacks from a single website.
The tool comprehensively returns all expected signals (CRM, sequencer, marketing automation) and specific booleans, fully covering the stated purpose.
Maintenance
Resources
Unclaimed servers have limited discoverability.
Looking for Admin?
If you are the server author, to access and configure the admin panel.
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