This server aggregates a company's go-to-market (GTM) signals by combining hiring-signal detection and tech-stack analysis into a single composite score (0–100).
Key capabilities:
Analyze a company domain (e.g., stripe.com) and return a flat JSON row with a composite GTM score, a signal strength label (low/medium/high), and a recommended action (e.g., prioritize, monitor, revisit)
Detect hiring signals — identifies GTM-related open roles and returns metrics like gtm_hiring_signal, signal_strength, and gtm_role_count
Detect tech stack signals — identifies CRM and GTM tools in use, returning fields like crm_detected, tech_stack_signal, and gtm_tool_count
Generate a plain-English summary (gtm_signal_summary) of the analysis via the include_summary option
Generate a detailed explanation by enabling explain_mode, which expands the summary into a thorough breakdown
Integrate directly into tools like Clay, CRMs, or AI agent workflows via the flat JSON output format
Requires an Apify API token; all analysis runs on Apify, not locally.
An MCP server that rolls a company's go-to-market signals into one composite score. It wraps the Mamba Labs GTM Signals Aggregator actor on Apify and returns a Clay-ready flat JSON row to any MCP client.
Related MCP server: mcp-gtm-hiring-signal-scraper
Give it a company domain and it runs hiring-signal and tech-stack detection together, then returns a single composite GTM score, a recommended action, and an optional plain-English summary. One call, one row, ready to drop into Clay, a CRM, or an AI agent workflow. All of the analysis runs on Apify. This package is a thin client that calls the actor and hands back the result.
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-signals": {
"command": "npx",
"args": ["-y", "@mambalabsdev/mcp-gtm-signals-aggregator"],
"env": {
"APIFY_TOKEN": "your-apify-token"
}
}
}
}Get your token at https://console.apify.com/account/integrations, paste it in, and restart Claude Desktop. The aggregate_gtm_signals tool will be available.
Node.js 18 or newer
An Apify account with an API token
"Give me the overall GTM signal score for stripe.com."
"How strong a GTM target is openai.com? Aggregate their signals."
"Score figma.com on hiring and tech stack, and explain why."
"Pull the composite GTM signal for datadoghq.com with a summary."
company_domain (required): the bare company domain, no https:// and no trailing slash. Example: stripe.com
sources (optional): which signals to aggregate: hiring, tech_stack, funding, events, workplace. Default ["hiring", "tech_stack"]. Each extra source is one more sub actor run on your own Apify account. The composite score is normalized over the sources you selected.
include_summary (optional): include a plain-English gtm_signal_summary in the output.
explain_mode (optional): if true, the summary becomes a longer, more detailed explanation.
The tool returns the actor's flat JSON row for the scanned company, including the composite GTM score, a recommended action, the underlying hiring and tech-stack signals, and an optional summary. See the Apify Store page for the full output schema.
{
"company_domain": "notion.so",
"composite_signal": "strong",
"composite_score": 82,
"recommended_action": "prioritize",
"gtm_hiring_signal": true,
"signal_strength": "high",
"gtm_role_count": 9,
"crm_detected": "salesforce",
"tech_stack_signal": "high",
"gtm_tool_count": 5,
"run_date": "2026-05-28"
}Combines hiring signals and tech stack detection in a single call
Flat row with composite_score, composite_signal, and recommended_action
Optional plain-English gtm_signal_summary
Designed for AI agent consumption
This server is a thin client and holds no analysis logic. For the complete input and output reference, pricing, and run history, see the Apify Store page:
https://apify.com/mambalabs/b2b-buying-signals-hiring-tech-stack-intent-for-clay
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.
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Built by Mamba Labs | npm | Apify Store
MIT
Built by Mamba Labs. https://apify.com/mambalabs
Aggregate a company's GTM signals into one composite score. Runs hiring-signal and tech-stack detection in a single call and returns a flat, Clay-ready JSON row with a composite GTM score, a recommended action, and an optional plain-English summary. Read-only; requires an APIFY_TOKEN and consumes Apify credits per call.
| Name | Required | Description | Default |
|---|---|---|---|
| company_domain | Yes | Bare company domain without https:// and without a trailing slash. Example: stripe.com | |
| include_summary | No | Include a plain-English gtm_signal_summary field in the output. Defaults to the actor's default when omitted. | |
| explain_mode | No | If true, gtm_signal_summary becomes a longer, more detailed explanation instead of a 1 to 2 sentence summary. |
Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?
Annotations already indicate readOnlyHint=true and destructiveHint=false. The description adds valuable transparency beyond that: it notes the tool 'requires an APIFY_TOKEN and consumes Apify credits per call', which are important behavioral traits. There is no contradiction with annotations.
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 three sentences long, each sentence adding essential information: core purpose, what it runs and returns, and read-only/auth/credit requirements. It is front-loaded with the most important information and contains no unnecessary 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 the tool has three parameters, no output schema, and comprehensive annotations, the description provides sufficient context. It explains the composite output, mentions the two signals combined, and clarifies auth and cost. The description covers all necessary aspects for an agent to use the tool correctly.
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%, so the schema already fully describes the three parameters. The description does not add additional meaning beyond stating the output includes a composite score and optional summary, which indirectly relates to the boolean parameters. No extra semantic detail is provided.
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 aggregates GTM signals into a composite score, specifying the verb 'aggregate' and resource 'GTM signals'. It also distinguishes itself by noting it runs both hiring-signal and tech-stack detection in a single call, making its purpose unambiguous.
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 context on when to use the tool (to aggregate GTM signals) and includes important caveats: it is read-only, requires an APIFY_TOKEN, and consumes credits. However, it does not explicitly mention when not to use it or alternatives, but there are no sibling tools, so the guidance is adequate.
Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.
Recent tool additions, removals, and schema changes observed during successful MCP inspections. Dates show when Glama detected each change.
v1.0.3aggregate_gtm_signalsWith only one tool, there is no possibility of confusion between tools. The tool's purpose is singular and clearly defined.
The single tool name follows a clear verb_noun pattern (aggregate_gtm_signals), which is consistent by default.
The server has only one tool, which is too few for the apparent scope of aggregating GTM signals. While the tool is comprehensive, it would benefit from being split into separate operations (e.g., for hiring signals and tech stack detection) or providing additional control tools.
The tool covers the main aggregation function, but there are likely gaps: no ability to fetch raw signals individually, no configuration or filtering options, and no tools for related actions like listing available signal types or checking data sources.
Unclaimed servers have limited discoverability.
If you are the server author, to access and configure the admin panel.
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