Model Context Protocol (MCP)

Model Context Protocol (MCP) is an open standard for connecting AI assistants to external data sources, tools, and systems. Developed by Anthropic, it provides a universal way for AI applications to access context and capabilities.

The problem MCP solves: Before MCP, every AI integration was custom. Connecting an AI to your CRM, database, or tools required bespoke code for each connection. This fragmented approach:

• Duplicated effort across developers
• Created inconsistent implementations
• Made switching AI providers difficult

MCP's approach: Standardize the interface between AI and tools. Write one MCP server for your system, and any MCP-compatible AI can use it.

Architecture:

AI Assistant ←→ MCP Protocol ←→ MCP Servers ←→ Your Systems
                                    ↓
                              - Databases
                              - APIs
                              - File systems
                              - Tools

Components:

MCP Hosts: AI applications that want to access external data (Claude Desktop, AI agents)

MCP Servers: Programs that expose capabilities via the MCP protocol (database connectors, tool providers)

Protocol: The standard communication format between hosts and servers

Capabilities:

• Resources: Files, documents, database records the AI can read
• Tools: Actions the AI can execute (search, write, send)
• Prompts: Pre-defined prompt templates for common tasks

For developers:

• Build once, use with any MCP host
• Pre-built servers for common systems
• Standard patterns and practices
• Growing ecosystem

For AI applications:

• Access richer context
• More capable tool use
• Easier integration with existing systems
• Provider portability

For organizations:

• Consistent AI integration patterns
• Reduced development time
• Easier maintenance
• Better security controls

For the ecosystem:

• Interoperability between tools
• Shared infrastructure
• Faster innovation
• Reduced fragmentation

Available MCP servers:

Data sources:

• PostgreSQL, MySQL databases
• Google Drive, Dropbox
• Notion, Slack
• GitHub repositories

Tools:

• Web search
• Browser automation
• File system access
• Code execution

Custom servers: Build MCP servers for your own systems:

• Internal databases
• Proprietary APIs
• Business tools
• Custom workflows

Example implementation:

const server = new MCPServer();

server.addTool({
  name: "search_customers",
  description: "Search customer database",
  parameters: { query: "string" },
  handler: async ({ query }) => {
    return await db.customers.search(query);
  }
});

Using existing servers:

1. Install an MCP-compatible host (Claude Desktop supports MCP)
2. Configure MCP servers in your settings
3. AI can now access those capabilities

Building MCP servers:

1. Define resources, tools, and/or prompts your server provides
2. Implement handlers for each capability
3. Use MCP SDK for your language (TypeScript, Python)
4. Test with MCP Inspector
5. Deploy and configure in hosts

Resources:

• MCP documentation: modelcontextprotocol.io
• Example servers: github.com/modelcontextprotocol
• SDKs: TypeScript, Python

MCP is early but growing. As more hosts and servers adopt the standard, the ecosystem's value compounds.