Renewals, churn signals, and contract validation, over a standard protocol.
Every GTM team runs into the same data problem.
A CSM is prepping for a renewal call. They need to know what features a customer is paying for, what they're actually using, whether they're near any limits, whether there's expansion signal. Today that's a tour through Salesforce, Stripe, product analytics, and maybe a Slack ping to engineering for a usage pull.
An AE just closed a complex enterprise deal. A week later, they want to confirm the custom entitlements match the order form. They file a ticket, or they just trust it worked.
A RevOps lead is building a churn model. They need to identify pro-tier customers who haven't hit 20% of their usage this month. They're pulling CSVs from three systems and hoping the IDs line up.
Each of these is a question an AI agent can answer in seconds, if the agent has access to the right data.
We shipped an MCP server for Schematic. Any MCP-capable agent (Claude, Cursor, your own) can:
Check which features a user or company is entitled to
Read plan details and usage state in real time
Operate with scoped, read-only API keys instead of full access
Customers are already building this directly into their GTM workflows to plan for renewal conversations, flag potential churn risks, and validate enterprise contracts are correctly implemented.
Three patterns we see most often.
Renewal prep. A CSM asks the agent to prep them for an upcoming renewal. The agent pulls the customer's current plan, the entitlements attached to it, usage against each metered feature for the last 90 days, any overrides granted in that window, and any custom terms. It flags under-used features, near-limit metrics, and expansion signals. The CSM walks in with an accurate picture in five minutes instead of five hours, and because the agent is pulling from Schematic directly, the picture reflects reality.
Churn signals. A RevOps lead wires the agent into Slack and asks it every Monday morning to surface pro-tier customers who haven't hit 20% of their usage for the month. The agent runs the join in real time across entitlements, plan tier, and usage. No CSV export, no dashboard build, no engineering ticket. The list shows up in a channel, and someone owns the follow-up by lunch.
Contract validation. For each closed-won custom deal, an agent pulls the plan attached to the company in Schematic and compares the entitlements to what the order form specified. Anything off gets flagged. Before finance catches it on the first invoice. Before the customer catches it when they hit a limit they shouldn't have hit.
Very little. Engineering instruments entitlements once in Schematic, the same integration work they'd do anyway. After that, GTM teams are self-serve. New agentic workflows don't require new engineering projects, because the data the agent needs is already exposed through the MCP server.
The security story matters here. Read-only API keys mean a GTM agent can see what it needs without the ability to change anything. The CSM's briefing agent, the RevOps churn detector, the ops validator, all running with scoped read access. No write surface exposed. No broad API keys floating in Slack workspaces.
Today the server exposes read operations. We're working on scoped write operations for trusted agents, which opens up a second category of GTM use cases. An agent that applies an override after a CSM approves it. An agent that grants a feature trial in response to a support escalation. An agent that extends a grace period on a custom plan when ops signs off.
The MCP server is live. Docs and setup details are on the Schematic developer portal.
Entitlements are going to be a core primitive for agentic software. The first place most teams feel that is internal, with their own CSMs, AEs, and RevOps leads getting faster and better at customer work. That's where Schematic MCP starts.
