Model Context Protocol
MCP is an open standard that connects AI systems with external applications. Think of MCP like a USB-C port for AI applications. Just as USB-C provides a standardized way to connect your devices to various accessories, MCP provides a standardized way to connect AI agents to different services.
MCP Host
AI Assistant / Agent
MCP Client
1:1 connection
MCP Server
Tools, Prompts, Resources
📖 MCP Terminology
The three roles in the Model Context Protocol ecosystem
MCP Hosts
AI assistants (like Claude or Cursor), AI agents, or applications that need to access external capabilities. The host is the top-level application that initiates and manages MCP connections.
ConsumerMCP Clients
Clients embedded within the MCP hosts that connect to MCP servers and invoke tools. Each MCP client instance has a single connection to an MCP server.
ConnectorMCP Servers
Applications that expose tools, prompts, and resources that MCP clients can use. Servers implement the MCP protocol to make capabilities available to AI agents.
Provider🌐 Remote vs. Local MCP Connections
The MCP standard supports two modes of operation
Remote MCP Connections
MCP clients connect to MCP servers over the Internet, establishing a connection using Streamable HTTP, and authorizing the MCP client access to resources on the user's account using OAuth.
Local MCP Connections
MCP clients connect to MCP servers on the same machine, using stdio as a local transport method. Ideal for development, testing, and single-user workflows.
✅ Best Practices
Guidelines for building reliable, secure, and agent-friendly MCP servers
Tool Design
Do not treat your MCP server as a wrapper around your full API schema. Instead, build tools that are optimized for specific user goals and reliable outcomes. Fewer, well-designed tools often outperform many granular ones, especially for agents with small context windows or tight latency budgets.
Scoped Permissions
Deploying several focused MCP servers, each with narrowly scoped permissions, reduces the risk of over-privileged access and makes it easier to manage and audit what each server is allowed to do.
Tool Descriptions
Detailed parameter descriptions help agents understand how to use your tools correctly — including what values are expected, how they affect behavior, and any important constraints. This reduces errors and improves reliability.
Evaluation Tests
Use evaluation tests ('evals') to measure the agent's ability to use your tools correctly. Run these after any updates to your server or tool descriptions to catch regressions early and track improvements over time.