MCP (Model Context Protocol) is an open protocol that lets AI models securely access external tools and data sources. As agents like Claude Code and Cursor become mainstream, MCP serves as the critical bridge connecting LLMs to the real world, significantly enhancing agent utility and autonomy.
The MCP ecosystem is expanding rapidly, with both an official specification and thousands of community-contributed MCP servers covering file operations, API integration, database queries, and more. Skills—higher-level abstractions over MCP—enable easier composition and reuse. Major players like AWS and NVIDIA have entered the space, with open-source implementations in Go, Python, and other languages.
Getting started & choosing well
Getting started: Choose an MCP-compatible client (e.g., Claude Desktop, Cursor) and configure an MCP server. For example, in Claude Desktop's config file, add: "mcpServers": {"excel": {"command": "node", "args": ["excel-mcp-server"]}}. Start with lightweight servers like Markdownify MCP.
Selection criteria: Evaluate maintenance activity, documentation quality, and protocol compatibility. GitHub Stars are a reference but not the only factor; prioritize repositories with CI/CD and regular updates. For production, prefer official or vendor-maintained versions.
Common pitfalls: 1) Client fails to discover MCP server — check path and permissions; 2) Protocol version mismatch — ensure client and server use the same MCP version; 3) Security — MCP servers can execute arbitrary code, so isolate the runtime environment.
Advanced: Use multi-model backends (e.g., Pal MCP Server) to manage multiple LLMs uniformly; wrap common workflows into pluggable Skills. For example, package "file-to-markdown → analysis → report" as a reusable Skill.
Frequently asked questions
What's the difference between MCP and Function Calling?
MCP is a standardized open protocol defining client-server communication, while Function Calling is a model-specific implementation. MCP is more general and cross-model; Function Calling typically depends on a specific API.
Are MCP servers safe?
They have full access to the host system, so they are not safe. Always run in sandbox or container, grant only necessary permissions. Community servers may contain malicious code; review before use.
How do I write my own MCP server?
Use the official SDKs (Python, TypeScript, Java, Go) for rapid development. Core tasks: implement a set of tools and resources following the MCP spec. Fork from template projects to start.
What's the relationship between Skills and MCP?
Skills are higher-level abstractions over MCP, combining multiple MCP tools into reusable task units. For example, a 'data extraction Skill' might invoke file MCP, database MCP, and API MCP.
What scenarios is MCP suitable for?
Scenarios where you need agents to perform real actions: automated workflows, code review, data pipelines. Not suitable for pure chat or scenarios without external interaction.
What are alternatives to MCP?
OpenAI's Plugin protocol, Anthropic's tool-use API, and custom tool systems in various frameworks. MCP's advantage is its openness and cross-platform nature.