Understand the Model Context Protocol — the open standard that powers AI agent tools.
The Model Context Protocol (MCP) is an open protocol created by Anthropic that standardizes how AI applications connect to external tools and data sources. Think of it as a USB-C port for AI — a universal interface that lets any compatible client talk to any compatible server.
Before MCP, every AI application had to build custom integrations for each tool or API. MCP provides a single standard so that a tool built once works with every MCP-compatible client — Claude Desktop, Cursor, Windsurf, and more.
MCP follows a client-server architecture. The AI application (client) connects to one or more MCP servers, each of which provides specific capabilities.
┌─────────────────┐ ┌──────────────────┐ │ AI Client │ │ MCP Server │ │ (Claude, etc.) │◄────────►│ (your tool) │ │ │ JSON-RPC │ │ │ Sends requests │ │ Exposes tools, │ │ to use tools │ │ resources, and │ │ │ │ prompts │ └─────────────────┘ └──────────────────┘
Communication happens over JSON-RPC 2.0. The client sends a request (e.g., “call this tool with these arguments”) and the server responds with the result. All messages follow a structured format:
// Client request
{
"jsonrpc": "2.0",
"id": 1,
"method": "tools/call",
"params": {
"name": "read_file",
"arguments": {
"path": "/Users/you/document.txt"
}
}
}
// Server response
{
"jsonrpc": "2.0",
"id": 1,
"result": {
"content": [
{
"type": "text",
"text": "Contents of the file..."
}
]
}
}MCP servers can expose three types of capabilities:
Functions the AI can call to perform actions. Tools are the most common primitive — they let the AI read files, query databases, send messages, manage infrastructure, and more.
Example: read_file, execute_query, send_slack_message
Data the AI can read for context. Resources are like files the AI can access — documentation, database schemas, configuration files, or any structured data that helps the AI understand your system.
Example: file:///schema.sql, db://tables
Pre-written prompt templates that guide the AI for specific tasks. Prompts are reusable instructions that help users get consistent results from tools.
Example: analyze_code, summarize_document
MCP supports two ways for clients and servers to communicate:
The client launches the server as a local subprocess and communicates over stdin/stdout. This is the most common transport — it's what you use when you configure a server with npx in your config file. No network required; everything runs locally.
The server runs as a web service and the client connects over HTTP. This is used for remote servers or servers that need to be shared across multiple clients. The client sends requests via HTTP POST and receives responses via an SSE stream.
Which transport to use?
Most tools on Hive Market use stdio. It's simpler, requires no network configuration, and keeps everything local. Use SSE only if your server needs to run remotely or serve multiple users.
MCP is an open standard with a detailed specification and growing ecosystem. Here are the official resources: