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MCP Ready
MCP, without the acronym soup

Give AI a safe way to do work.

MCP is a common way for AI products to discover and use your existing data, software, and actions—without building a different integration for every assistant.

Understand MCP See real workflows
A connected AI workflow
Brief me on Acme before tomorrow’s meeting. Include open invoices and recent support issues.
AI
selects relevant tools

CRM

get_account

Billing

list_invoices

Support

search_tickets

MCP returned 3 sourced results
ONE REQUESTTHREE SYSTEMSONE CONTROLLED PATH

The label changes. The underlying idea often does not.

AppsConnectorsIntegrationsPluginsMCP servers

These product terms are not exact synonyms, but many now package MCP underneath. See the translation guide.

Same protocol, different product language

See how the major AI tools actually use MCP.

Compare ChatGPT, Claude, Microsoft Copilot, Cursor, GitHub Copilot, and Google Gemini—what each calls a connection, which transports it supports, and where administration and progressive discovery fit.

Compare AI products

MCP in 30 seconds

One shared contract between AI and the systems you already use.

MCP does not replace your software, databases, APIs, or permissions. It gives compatible AI products a consistent way to understand and use them.

How MCP fits with APIs
  1. 01

    Describe capabilities

    A server exposes focused tools such as find_contract or create_ticket, with clear inputs and outputs.

  2. 02

    Choose what fits

    The AI host decides which connected capability is relevant—while applying the user’s identity, permissions, and approval rules.

  3. 03

    Return a result

    Your existing system does the work. A structured result comes back for the AI to explain, cite, combine, or present for review.

From copying to connecting

The useful part is not the chat. It is the controlled access behind it.

Without a connection, people move data by hand. With a well-built MCP integration, the AI can fetch current information and take approved actions at the source.

Before

MANUAL CONTEXT
CRM export
Copied email
PDF upload
→→→

One-off AI chat

Stale copies, missing provenance, no path back

With MCP

LIVE + GOVERNED
CRM system
Email account
Document store
MCP

Compatible AI host

Current data, scoped access, structured results

What this looks like in practice

Start with a job people already do.

Explore all examples →
Research

Build a sourced brief across documents, email, and the web.

Read, search, compare, and preserve a trail back to every source.
Customer ops

Investigate a customer issue, then prepare the next action.

Combine account history, support tickets, orders, and approval-aware updates.
Finance admin

Collect records and prepare an accountant-ready package.

Find missing documents and organize evidence—without delegating professional judgment.

A quiet implementation detail that matters

The AI does not need every tool manual on every turn.

Large catalogs can crowd the model’s context and make selection harder. Modern hosts can keep a small catalog visible, search it, and load only the relevant tool definitions.

See progressive tool discovery →

Load everything

Every schema competes for attention before the user’s request is handled.

Discover as needed

search_tools(“open invoices”)
↓
billing.list_invoices
crm.get_account

Only the useful definitions enter the working context.

Ready means more than connected

Start narrow. Design the permissions. Test the real prompts.

The best first MCP project is a specific, valuable job with a clear system of record, a limited tool surface, and an obvious human owner.

Read the implementation guide Work with Strata