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mcp-gtm-suite

by mambalabsdev
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TypeScript
Remote

Aggregate GTM Signals

aggregate_gtm_signals
Idempotent

Combine a company's GTM signals into one composite score and recommended action, returning a Clay-ready JSON row for outbound prioritization.

Instructions

Aggregate a company's GTM signals into one composite score. Runs hiring and tech-stack detection by default, plus funding, events, or workplace when named in sources, in one call and returns a composite score, recommended action, and optional summary as a flat, Clay-ready JSON row. Reads public data and writes nothing; requires an APIFY_TOKEN and consumes Apify credits per call.

Input Schema

NameRequiredDescriptionDefault
sourcesNoWhich signals to aggregate. Defaults to hiring and tech_stack. Each extra source adds one sub actor run to the bill and one dimension to the composite score, which is normalized over the sources selected.
explain_modeNoIf true, the summary becomes a longer, more detailed explanation.
company_domainYesBare company domain, e.g. stripe.com
max_charge_usdNoOptional spending cap for this call, in USD, passed to the actor as max_charge_usd. The run stops before any charge that would take it past this amount and keeps every row already charged. Leave empty to rely only on the maximum charge per run set on your Apify account.
include_summaryNoInclude a plain-English gtm_signal_summary.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed1 schema field changedv1.14.0
    • addedInput schema / properties / max_charge_usd
      Added value: +{
+  "description": "Optional spending cap for this call, in USD, passed to the actor as max_charge_usd. The run stops before any charge that would take it past this amount and keeps every row already charged. Leave empty to rely only on the maximum charge per run set on your Apify account.",
+  "minimum": 0,
+  "type": "number"
+}
  2. Changed1 schema field changedv1.1.1
    • addedInput schema / properties / sources
      Added value: +{
+  "description": "Which signals to aggregate. Defaults to hiring and tech_stack. Each extra source adds one sub actor run to the bill and one dimension to the composite score, which is normalized over the sources selected.",
+  "items": {
+    "enum": [
+      "hiring",
+      "tech_stack",
+      "funding",
+      "events",
+      "workplace"
+    ],
+    "type": "string"
+  },
+  "type": "array"
+}
  3. First observedv1.0.3

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

Adds substantial behavioral context beyond annotations: requires an APIFY_TOKEN, consumes Apify credits per call, reads public data and writes nothing, and each extra source adds a sub-actor run to the bill. There is mild tension with readOnlyHint=false (the call does trigger billable external runs), but the description is transparent about those side effects rather than hiding them.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness4/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Front-loaded with the core purpose, then supporting detail on defaults, return shape, and cost/auth. Efficient overall, though the second sentence is dense and packs defaults, costs, and return format into one long clause.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

No output schema exists, and the description compensates by naming the return contents (composite score, recommended action, optional summary as a flat Clay-ready JSON row). Combined with its auth/cost disclosure, an agent has enough to invoke it correctly, though pagination/error behavior is unstated.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters3/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented in the schema, including defaults, the billing effect of extra sources, and max_charge_usd semantics. The description largely restates this (defaults to hiring/tech-stack, sources add cost and dimensions), so it adds little beyond the schema — baseline 3.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose5/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Aggregate a company's GTM signals into one composite score') and clarifies it combines hiring + tech-stack (plus optionally funding/events/workplace) 'in one call'. This distinguishes it from single-signal siblings like scan_gtm_hiring_signals and detect_gtm_tech_stack without opening either schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Explains the aggregation context clearly: hiring and tech-stack run by default, others only when named in sources, all in a single call. This implies when to prefer the aggregator over individual scanners, but it never explicitly says 'use this instead of calling the single-signal tools separately' or names any when-not condition.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

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