# An MCP server is a starting point, *not a solution.*

*Composite MCP servers and the playbook layer on Workato: orchestration, governance, and context control that take enterprise AI from demo to production.*

Canonical: https://onesolve.io/solutions/enterprise-mcp

Enterprise MCP · Practice

MCP (Model Context Protocol) lets AI agents read and act in your business systems, workflows, and data.
What it connects to, how it's governed, and whether your team actually trusts it. That's the build.
**We've done it enough times to know where it breaks.**

[Book a working session →](../contact-us.html) [See all solutions →](../solutions.html)

The architecture gap

## Why native MCP servers stall at the enterprise boundary.

Native servers are built for single-system interactions. Enterprise AI work isn't. Here's where the native
servers break, and what a composite layer fixes.

Orchestration

### Your team stops being the router.

One composite server orchestrates your entire stack. Multi-system workflows resolve in a single turn: the LLM asks once, the server handles routing, sequencing, and fallbacks.

No more human handoffs. No IT permanently on-call.

Governance

### One audit trail. Board-defensible.

Permissions set once at the composite layer, enforced across every connected system. Every tool call logged.

Every action attributable, instead of five siloed auth models stitched together at review time.

Context

### Targeted context. Optimal tokens.

The composite layer filters and compresses context before it reaches the model, passing only what the work needs instead of four raw payload dumps.

Token costs stay predictable. Latency drops.

What we build

## We build the MCPs. *We build the playbooks.*

Most teams are stuck between two missing layers: the MCP server layer (the curated tools your LLM can
actually call) and the playbook layer (the project instructions, knowledge, and output templates that turn
those tools into a workflow your team runs). Where a vendor MCP doesn't exist, we build it from the connector
up. Where one exists but doesn't fit your data model, we extend it. Then we build the playbook layer on top.

01 · Route

### Playbook activates

The chat router reads the prompt and picks the workflow that matches the use case. Chained tool calls, not sequential prompts.

02 · Enrich

### Knowledge layer applies fit criteria

Reads your account-fit definition, pulls recent news, headcount, and tech-stack signals, and scores against your buyer.

03 · Check

### CRM check and draft

Searches Salesforce for existing contact and account records. Drafts what's missing for the rep to confirm before any write.

04 · Assemble

### Brief assembled in your format

An output template renders the same shape for every rep, every account, every time. It lands in Slack for the rep to act on.

In production

## Other patterns we've put into production.

GTM

### Composite GTM playbook

Salesforce + Gong + Salesloft

Morning brief, post-call CRM update, and closed-lost re-engagement. One prompt, three systems behind it.

RevOps · Finance

### Quote-to-Cash orchestration

Salesforce → NetSuite

Closed Won to Sales Order in under a minute: customer validation, line-item mapping, and upsert logic. Re-run safe at any volume.

IT Ops · People Ops

### IT ticket deflection

Okta + Google + Jira + HRIS

Password resets, app provisioning, compliance scans, and onboarding profile creation through one chat. Human-in-the-loop on writes.

When MCPs matter for you

## Five signals. *If three are true, we should talk.*

1. **Leadership approved the AI rollout.** Someone is now on the hook to answer "what does it actually connect to?", and "we'll figure that out later" is running out of runway.
2. **You already have Workato deployed,** and the AI work is now landing on the same team's plate.
3. **A SaaS vendor just quoted you $20K+ per year** for their native MCP, and the security review hasn't started yet.
4. **The internal AI hackathon produced something real.** The prototypes worked. Nobody has asked yet who turns them into something the team runs every day.
5. **Your CIO has to present an AI architecture plan to the board,** and "we're evaluating tools" isn't going to be the answer.

What clients discover

## Production AI. *Not just a demo.*

> "For our internal AI hackathon, OneSolve quickly helped us connect Salesloft, Gong, and Salesforce via MCP servers. The result was a real GTM playbook, not just a demo, that has shaped how we think about AI internally."
> Henry Hung · Head of GTM Systems · Faire  ·  5.0, Workato Partner Directory

How we govern it: every output is reviewed before it acts, every tool call is logged to your SIEM, and
agentic identity is scoped per recipe, not a borrowed user token. Draft-never-send by default.
[See all solutions →](../solutions.html)

## Working session. *90 minutes.*

We sketch your MCP server architecture against the connectors you have, identify the first playbook to build,
and outline the knowledge layer your data model needs. You leave with a document we wrote during the call,
not a follow-up deck.

[Book a working session →](../contact-us.html) [Email us directly](mailto:hello@onesolve.io)