GTM Stack AI-Readiness Score
Add your GTM tools. We score each one on API coverage, MCP server availability, data export, AI-native features, and webhooks — then grade your stack 0–100.
Your GTM stack
Common GTM tools:
API coverage is the floor, not the ceiling.
The GTM Stack Audit skill in the operator playbook scores your stack against your actual ICP, motion, and spend — not just API availability. 42 Claude Code skills, $39 one-time, runs on your own CRM data.
Get the operator playbook ($39) →What is GTM stack AI-readiness?
AI-readiness measures how usable your GTM stack is by AI agents like Claude. An agent can only help with the tools it can actually read from and write to. A stack of powerful tools that lock their data behind a UI is nearly useless to an agent; a stack with open APIs and MCP servers lets an agent audit pipeline, score accounts, and update records directly.
How the AI-readiness score works
Each tool is scored 0–100 across five dimensions: API coverage (30 pts), MCP server availability (25 pts), data export (20 pts), AI-native features (15 pts), and webhooks (10 pts). Your stack score is the average across all your tools.
MCP coverage — the share of your tools that expose a Model Context Protocol server — is called out separately because it's the fastest-moving lever for agent usability. A tool with an official MCP server can be dropped into Claude and used immediately.
What is MCP, and why does it matter?
The Model Context Protocol (MCP) is an open standard that lets AI agents read and act on a tool directly, without custom API glue. As more of your stack becomes MCP-covered, more of your GTM workflows — pipeline audits, ICP scoring, enrichment, forecasting — become things an agent can run on your real data.
A score of 85+ is Claude-ready, 65–85 is mostly ready with an MCP gap to close, 45–65 has real gaps, and below 45 means agents are largely blocked. Median GTM stacks land around 62 with roughly 45% MCP coverage.
Frequently asked questions
What makes a GTM tool AI-ready?
A full REST API, an MCP server (official or community), unrestricted data export, AI-native features, and webhooks. The first two matter most: without programmatic read/write access, an agent can't use the tool at all.
What is an MCP server?
An MCP (Model Context Protocol) server exposes a tool to AI agents over an open standard, so a model like Claude can read and act on it directly. It's the difference between an agent being able to query your CRM and being locked out.
How is the AI-readiness score calculated?
Each tool scores across five weighted dimensions — API (30), MCP (25), export (20), AI-native features (15), webhooks (10) — for a 0–100 tool score. Your stack score is the average across all the tools you add.
What is a good AI-readiness score?
85+ is Claude-ready, 65–85 is mostly ready with an MCP gap to close, 45–65 has real gaps, and below 45 means agents are largely blocked. Median GTM stacks land around 62 with about 45% MCP coverage.
Why does AI-readiness matter for GTM?
AI agents are becoming the layer that runs GTM work — enrichment, scoring, audits, forecasting. A stack they can't access caps how much you can automate. AI-readiness tells you which tools are unblocking agents and which are holding you back.