Getting Started with MCP

Model Context Protocol (MCP) is an open standard that enables AI assistants to connect with external tools, data sources, and services. MCP servers expose capabilities that AI agents can call — think of them as plugins for your AI.

What is an MCP Server?

An MCP server is a lightweight process that implements the MCP specification. It exposes tools (callable functions), resources (data sources), and prompts (reusable templates) to any MCP-compatible client.

Common MCP servers give AI agents access to:

• File systems — read and write local or remote files
• APIs — interact with GitHub, Slack, databases, and more
• Code execution — run scripts in sandboxed environments

Finding Quality MCP Servers

Not all MCP servers are created equal. Before installing one, check:

1. Quality score — a score above 80 means the server is well-documented, actively maintained, and verified by the community
2. Maintenance status — look for servers with commits in the last 90 days
3. Test coverage — servers with test files are less likely to break unexpectedly

SkillRepo aggregates quality signals from GitHub, npm, and the official MCP registry so you can compare options at a glance.

Installing an MCP Server

Most MCP servers are distributed as npm packages. Install with:

npx @modelcontextprotocol/install <server-name>

For example, to install the GitHub MCP server:

npx @modelcontextprotocol/install github-mcp

After installation, add the server to your MCP client configuration (e.g., Claude Desktop's claude_desktop_config.json):

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

Next Steps

Browse the catalog to find MCP servers for your use case. Filter by type, quality tier, or maintenance status to narrow down your options quickly.