MCP — the Model Context Protocol — is an open standard that lets AI agents plug into tools, data sources, and apps the same way, so any agent can talk to any compliant tool.
MCP — Model Context Protocol — is an open standard, originally introduced by Anthropic, that defines how AI agents talk to external tools and data sources. Before MCP, every agent platform had to write custom code for every tool integration: a different connector for Gmail, a different one for Slack, a different one for your CRM. MCP collapses that into one protocol.
The widely-used analogy is USB-C. Before USB-C, every device had its own connector and you needed an adapter for everything. After USB-C, anything that supports the standard talks to anything else that supports it. MCP is the same idea for AI tooling — any agent platform that speaks MCP can connect to any tool that exposes an MCP server.
Practically, MCP matters because it reduces vendor lock-in. If your tools support MCP and your agent platform supports MCP, swapping either one becomes vastly simpler. It also accelerates the rate at which new integrations appear: any team can publish an MCP server for their service and instantly become available to every MCP-aware agent.
An accountant builds a bookkeeping agent on Squidgy and wants it to read from Xero, write to a Google Sheet, and post summaries to Slack. With MCP, all three tools have public MCP servers — the agent connects to each one through the same protocol. No custom code, no per-integration developer work.
MCP is the difference between an agent ecosystem that splinters and one that compounds. Without it, every agent platform has to negotiate every integration separately, and small platforms get squeezed out by the giants who can afford the engineering. With it, the smallest builder gets the same integration breadth as the largest.
For non-technical founders, MCP means you don't have to wait for your agent platform to ship a Stripe integration, a Notion integration, a Calendly integration. If those services have an MCP server (and most major ones do or will), your agent can already use them.
The risk is fragmentation around the standard itself — different platforms implementing slightly different MCP semantics, breaking interop. So far the standard has held cleanly because the major players (Anthropic, OpenAI, the open-source ecosystem) are aligned on it.
Squidgy supports MCP. You can plug any MCP-compatible tool into your Squidgy agent — public servers, your own internal MCP servers, or third-party ones. Conversely, Squidgy agents can be exposed as MCP servers themselves, so they can be used by any MCP-aware client (including ChatGPT, Claude Desktop, and similar).
The practical impact for builders: when a new useful tool publishes an MCP server, your existing agent can connect to it without us shipping a special integration. It's the same reason USB-C ended the dongle era.
Anthropic introduced MCP in late 2024 as an open standard. It's been adopted across the ecosystem since — OpenAI, Microsoft, Google, and the open-source community all support it.
No. MCP is an open standard, not tied to any one model or company. Any AI agent platform can implement it; any tool can expose an MCP server. ChatGPT, Claude, Gemini, and others all work with MCP-compliant tools.
Anything that has an MCP server — and that list grows weekly. Major examples in 2026 include Gmail, Slack, GitHub, Notion, Linear, Stripe, Google Workspace, your file system, and many vertical SaaS tools. You can also write your own MCP server for an internal system.
Not on Squidgy. The platform handles MCP plumbing in the background — you just pick the tools you want from a list, authorise them, and the agent uses them. The MCP layer is invisible at your end.
Glossary
What is Tool calling?
Tool calling is when an AI agent decides to use a tool — like sending an email, looking up a record, or charging a card — instead of just talking about it.
Glossary
What is AI agent?
An AI agent is software that takes a goal, decides what steps to take, uses tools to do them, and carries the work out with little or no human prodding between steps.
Glossary
What is Agent builder?
An agent builder is a tool for creating AI agents — defining what they do, what tools they can use, and how they decide — without writing all the code yourself.
Glossary
What is RAG (retrieval-augmented generation)?
RAG — retrieval-augmented generation — is when an AI looks up relevant info from your documents before answering, so its replies are grounded in your actual content instead of just its training data.
No code. Hands-on onboarding from the team in your first cohort.