An honest read on how Clearskies and Building your own MCP servers differ — and how to pick based on where your revenue motion lives.
Building MCP servers in-house means you decide exactly what data the model can touch, in exactly what shape, with exactly the policies you write.
If your team is comfortable owning MCP infrastructure, deploying inside your VPC keeps the data path entirely under your control.
An in-house Salesforce MCP returns Salesforce data. A Gong MCP returns Gong data. The AI still has to figure out which records, activities, and signals belong to the same customer story. Clearskies resolves identity, timeline, and relationships before the AI ever queries.
Quoting the homepage: 'Our Salesforce MCP stopped working. We spent three days of 12-hour days trying to figure it out, and it pulled our VP RevOps off other work.' Custom MCPs become infrastructure someone has to monitor when APIs change, scopes shift, or schemas evolve.
Every prompt with a stack of custom MCPs spends tokens rebuilding context that should already exist: account history, activity maps, timelines, call evidence, Slack threads. Clearskies pre-resolves this once.
A Salesforce MCP returns Salesforce data. A Gong MCP returns Gong data. Nothing in that stack models your methodology, stages, exit criteria, or team structure — much less observes activity against the model and refines it. To get there yourself, you'd build a process schema, observation logic against it, refinement logic, versioning, and wire all of it into every prompt. Clearskies ingests the process as first-class context and refines it automatically from observed signal.
With multiple custom MCPs, the model has to stitch results across servers at query time — and gets it wrong often enough to matter. Clearskies hands the model one resolved view that already spans the stack.
Connect your systems to Clearskies in minutes. Build comparable MCPs in-house and you're looking at engineering sprints per integration, plus ongoing maintenance.
| Clearskies | Building your own MCP servers | |
|---|---|---|
| Identity resolution across systems | Pre-computed; resolved once across every source | You build it — and the model re-resolves at query time |
| Timeline construction | Pre-built per account, deal, and person | You build it — or the model approximates per query |
| Gap detection | First-class — surfaces what's missing | Not standard — a separate build on top of each MCP |
| Sales process modeling and refinement | Ingests methodology, stages, exit criteria, team structure — refines automatically from observed activity | Not part of MCP — you'd build the process schema, observation, and refinement logic on top |
| Token cost per query | Lower — pre-resolved graph passed in once | Higher — context rebuilt per query from raw connector results |
| Engineering required to maintain connectors | None — managed | Engineer time per integration plus ongoing maintenance |
| Connector monitoring + on-call | Clearskies handles it | Your team |
| Cross-system queries | One resolved answer spanning every source | Model stitches across MCPs at query time; brittle and lossy |
| Time to first working workflow | Minutes to connect; days to ship | Engineering sprints per integration |
| Customizability of what's exposed | Workspace-scoped + RBAC + per-source rules | Unlimited — you decide every byte |
| Data path control / VPC option | Enterprise VPC option available | Full control by default |
“I have Clearskies MCP connected to Claude which also connects into Amplitude, M365, and Atlassian. I'm running daily and weekly CEO Briefs on sales, competition, product sentiment, customer success, and customer health. I'm getting a level of insight that would have been impossible or massively labor intensive before.”
— Jake Olsen, CEO at Stratus
Comparing other AI tools too? See every comparison. Or visit Building your own MCP servers.
Same data, both tools, one workflow you don’t fully trust today. See the difference on something real.