MCP Servers Explained: How to Connect Any AI to Your Business Tools

If you have been following the AI space, you have probably noticed that every tool wants to connect to everything else. Your CRM wants to talk to your email. Your ad platform wants to talk to your analytics. Your project management tool wants to talk to your calendar. The result is dozens of one-off integrations, each built slightly differently, each with its own quirks and limitations.

MCP, short for Model Context Protocol, is Anthropic's answer to that mess. Released as an open standard in November 2024, it provides a single, universal way for AI applications to connect to external tools and data sources. Claude, ChatGPT, Gemini, GitHub Copilot, and dozens of other AI tools all support it now. The analogy Anthropic uses is USB-C: before USB-C, every device had its own charger and cable. USB-C gave us one port that works for everything. MCP does the same thing for AI integrations.

The practical result: instead of each AI platform needing custom-built integrations for every tool (Salesforce, HubSpot, Google Ads, Slack, your internal database), each tool just needs one MCP server. Once that server exists, any AI application that supports MCP can connect to it. Claude, ChatGPT, Gemini, Copilot, Cursor, Windsurf. Build the bridge once, use it everywhere.

What makes this genuinely significant is that MCP did not stay an Anthropic-only thing. OpenAI adopted it for ChatGPT and the OpenAI Agents SDK. Google added support to Gemini and Android Studio. Microsoft integrated it into Copilot, VS Code, and Windows 11. Amazon built it into Q Developer. When every major AI company starts building on the same open standard instead of creating their own, that tells you something about the value of the approach. If you invest time connecting your tools via MCP today, those connections work across every major AI platform, not just one.

You do not need to understand MCP's architecture to use it. But having a basic mental model helps you make better decisions about which servers to use and when to build custom ones. Here is the simplified version.

MCP servers expose three types of capabilities to the AI:

Servers can run in two ways. Local servers run on your machine as a subprocess and communicate through standard input/output. They are simple to set up and keep all data on your computer. Remote servers run on the internet as HTTP services and authenticate through OAuth. They are better for team environments and SaaS integrations where everyone needs access to the same tools.

MCP went from an Anthropic side project to an industry standard faster than anyone expected. Here is where things stand today:

The adoption timeline tells an interesting story. Anthropic released MCP in November 2024. Within four months, OpenAI integrated it into ChatGPT and the OpenAI Agents SDK. Google followed with Gemini and Android Studio support. Microsoft announced native MCP in Windows 11, VS Code, and Copilot. Amazon added it to Q Developer. Cursor, Windsurf, Sourcegraph Cody, and dozens of independent AI tools adopted it. By December 2025, Anthropic donated MCP to the Agentic AI Foundation under the Linux Foundation, co-founded with Block and OpenAI, with backing from Google, Microsoft, AWS, and Cloudflare.

That last part matters for businesses. When a protocol is governed by a neutral foundation rather than a single company, it is much safer to build on. You are not betting on one vendor. The industry has collectively decided that MCP is the standard for connecting AI to tools, and that is unlikely to reverse at this point.

The server ecosystem has also reached critical mass. An official registry launched in September 2025, and alongside community directories like mcp.so and GitHub's own MCP registry, finding a server for your specific tool is becoming as easy as searching an app store. Major companies including GitHub, GitLab, Atlassian, Sentry, Snowflake, HubSpot, and Figma have all released official MCP servers for their platforms.

There are thousands of MCP servers out there, but most businesses only need a handful. Below are the servers organized by business function. Tap any category to see the specific servers available and whether they are official (maintained by the platform itself) or community-built.

There are three main ways to get MCP servers up and running, ranging from completely non-technical to mildly technical.

Option 1: Desktop Extensions (Easiest)

Desktop Extensions are MCP servers packaged into single-click installable bundles. They work like browser extensions: open Claude Desktop, go to Settings, browse the extension directory, click install. No terminal, no dependencies, no config files. The extension handles everything.

Anthropic open-sourced the extension format so other AI applications can adopt it. Extensions from the official directory auto-update, and the permission system lets you see exactly what each extension can access before you install it.

Option 2: Remote Server URL (Moderate)

Many MCP servers now run remotely and just need a URL. In Claude Desktop, go to Settings, then Connectors, then “Add custom connector” and paste the server URL. ChatGPT, Gemini, and Copilot have similar configuration flows in their settings. If the server requires authentication (most do), the platform will walk you through an OAuth login flow. No local installation needed.

Option 3: Manual JSON Configuration (Most Flexible)

For local servers or when you need full control over the setup, you edit a JSON config file. This is where you point Claude at a specific server command and pass in any required environment variables like API keys.

{
  "mcpServers": {
    "github": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-github"
      ],
      "env": {
        "GITHUB_TOKEN": "your-token-here"
      }
    }
  }
}

Where to Find Servers

Pre-built servers cover the major platforms, but every business has tools that are specific to their industry or workflow. Maybe you use a niche CRM, a proprietary inventory system, or an internal database that no public MCP server supports. That is when building a custom server makes sense.

The scope of a custom MCP server is smaller than most people expect. You are not building a full application. You are building a lightweight wrapper that translates between the MCP protocol and your tool's existing API. If your tool has a REST API (and most modern software does), the MCP server is essentially a thin translation layer on top of it.

Official SDKs also exist for Kotlin, Java, C#, Go, Ruby, Rust, Swift, and PHP. The MCP project maintains all of these, so you can build in whatever language your team is most comfortable with.

One practical tip: you can use Claude itself to help write your MCP server. Describe what your tool's API does, what endpoints you want to expose, and what actions you want Claude to be able to take. Claude can generate most of the server code, which you then test and refine. We have seen developers go from zero to a working custom MCP server in a single afternoon using this approach.

from mcp.server.fastmcp import FastMCP

mcp = FastMCP("my-crm-server")

@mcp.tool()
def search_contacts(query: str) -> str:
    """Search CRM contacts by name or email."""
    results = crm_api.search(query)
    return json.dumps(results)

@mcp.tool()
def update_deal_status(
    deal_id: str, status: str
) -> str:
    """Update the status of a deal."""
    result = crm_api.update_deal(
        deal_id, status
    )
    return json.dumps(result)

Building a custom MCP server is a focused development project, but it is still a development project. If your team does not have the bandwidth or the technical skills, we build custom MCP servers for businesses. Tell us what tools you need connected, and we will build the server, test it, and hand it over. No subscriptions, you own the code. Let us know what you want to connect.

MCP servers are powerful because they give AI platforms like Claude, ChatGPT, and Gemini access to your real tools and real data. That power comes with real security considerations. You should not be paranoid about it, but you should be informed.

OWASP (the organization behind web security standards) published a dedicated MCP Top 10 risks list in 2025. Here are the ones that matter most for business users: