Model Context Protocol (MCP) is an open standard created by Anthropic that lets AI assistants connect to external tools and data sources through a single, unified interface.
How MCP works
MCP follows a client-server architecture. The client is the AI assistant (Claude Desktop, Cursor, etc.) and the server is any external tool or data source that wants to expose functionality to AI.
When you connect an MCP server to an AI client, the server tells the client what tools it has available. The AI can then decide when to use those tools during a conversation. For example, when you ask Claude “search my bookmarks for articles about testing”, Claude recognises it should use the ContextBolt MCP server’s search tool, sends the query, and incorporates the results into its response.
The key insight is that MCP standardises this connection. Before MCP, every AI tool integration required custom code. A bookmark tool connecting to Claude needed different integration code than the same tool connecting to Cursor. MCP replaces all of that with a single protocol.
Why MCP matters
MCP solves what Anthropic calls the “N times M integration problem”. Without a standard, N AI clients connecting to M tools requires N times M custom integrations. With MCP, each client implements the protocol once, and each tool implements it once, and they all work together.
For users, this means tools you connect to one AI assistant automatically work with others. If you set up ContextBolt’s MCP server for Claude Desktop, the same server works with Cursor, Windsurf, or any other MCP-compatible client like Cline or Claude Code.
For developers, MCP reduces the cost of making tools AI-accessible. Instead of building separate integrations for each AI platform, you build one MCP server.
MCP in practice
The MCP ecosystem has grown rapidly since its release. By early 2026, there are over 10,000 public MCP servers covering everything from database access and file management to API integrations and specialised tools.
Common MCP use cases include:
- Data access: searching databases, files, or APIs from within AI conversations
- Tool execution: running code, managing tasks, or controlling applications through AI
- Context enrichment: giving AI assistants access to relevant information like bookmarks, documents, or project context
ContextBolt uses MCP to expose your social media bookmarks as a searchable resource. When connected, AI assistants can search your saved content from Twitter/X, Reddit, and LinkedIn without you leaving the conversation.
The technical bits
MCP uses JSON-RPC 2.0 over standard I/O (stdio) for local connections or Server-Sent Events (SSE) over HTTP for remote connections. Servers can expose three types of capabilities:
- Tools: functions the AI can call (like searching bookmarks)
- Resources: data the AI can read (like a list of available bookmark collections)
- Prompts: pre-built prompt templates for common tasks
The protocol handles authentication, capability negotiation, and error handling. SDKs are available in TypeScript (@modelcontextprotocol/sdk) and Python (mcp), making it straightforward to build either servers or clients.