Published February 23, 2026 · 20 min read
Model Context Protocol (MCP) has quietly become one of the most important technologies in the AI ecosystem. If you have used Claude Code, Cursor, or any agentic AI tool that connects to external services, you have likely interacted with an MCP server without knowing it. MCP is the open standard that lets AI models talk to databases, APIs, file systems, and any other tool or data source through a unified interface.
In 2026, MCP adoption has reached a tipping point. Thousands of MCP servers exist for everything from GitHub and Slack to Postgres databases and Kubernetes clusters. Understanding MCP is no longer optional for developers working with AI — it is a core skill. This guide covers everything from the fundamentals to building your own MCP server from scratch.
Model Context Protocol (MCP) is an open standard, originally created by Anthropic and released in late 2024, that defines how AI applications communicate with external tools and data sources. Think of MCP as a universal adapter between AI models and the rest of the software world. Before MCP, every AI tool had to build custom integrations for every service it wanted to connect to. MCP replaces that N-times-M problem with a single standardized protocol.
The analogy most people use is USB. Before USB, every peripheral device had its own proprietary connector. USB created a universal standard, and suddenly any device could connect to any computer. MCP does the same thing for AI: any AI application that supports MCP can connect to any MCP server, and any MCP server can serve any MCP-compatible AI application.
The protocol follows a client-server architecture. The MCP client is the AI application — Claude Code, Cursor, or your custom AI agent. The MCP server is a lightweight program that exposes specific capabilities (tools, data, prompts) through the standardized protocol. The client discovers what the server offers, and the AI model can then use those capabilities as needed.
"MCP is to AI tools what HTTP is to the web. It's the plumbing that makes everything work together." — Developer community consensus
MCP solves several critical problems that have limited AI tool adoption:
The integration problem. Before MCP, if you wanted your AI assistant to access your company's Jira, Slack, GitHub, and database, someone had to build four separate custom integrations. With MCP, each service has a single MCP server, and any MCP-compatible AI client can connect to all of them. Build once, use everywhere.
The context problem. AI models are only as useful as the context they have. A model that cannot access your codebase, documentation, or databases is limited to general knowledge. MCP lets AI models pull in exactly the context they need, when they need it, from any connected data source.
The vendor lock-in problem. Custom integrations tied you to specific AI providers. If you built a Claude integration with your tools, switching to GPT meant rebuilding everything. MCP is provider-agnostic. Your MCP servers work with any client that supports the protocol.
The security problem. MCP provides a structured way to control what AI models can access. Instead of giving an AI model raw API keys and hoping for the best, MCP servers expose only specific, well-defined operations with clear permission boundaries. The human stays in control of what the AI can do.
The ecosystem problem. Because MCP is an open standard with a growing community, thousands of pre-built servers already exist. Need your AI to access a Postgres database? There is an MCP server for that. Need it to manage Docker containers? There is an MCP server for that. The ecosystem effect means you rarely need to build from scratch.
MCP uses a JSON-RPC 2.0-based message protocol over two primary transport mechanisms:
stdio (Standard I/O): The most common transport for local MCP servers. The client spawns the server as a child process and communicates through stdin and stdout. This is how Claude Code connects to local MCP servers — fast, secure, and requires no network configuration.
SSE (Server-Sent Events) over HTTP: Used for remote MCP servers. The client connects to the server via HTTP, and the server pushes messages back through an SSE stream. This enables MCP servers running on remote machines, in containers, or as cloud services.
The lifecycle of an MCP connection follows these steps:
initialize request with its capabilities and protocol version. The server responds with its own capabilities.tools/list, resources/list, and prompts/list to discover what the server offers.tools/call request with the tool name and arguments. The server executes the operation and returns the result.resources/read to pull in data as context for the AI model. Resources are read-only data like file contents, database records, or API responses.What makes MCP elegant is that the AI model itself decides when and how to use the available tools. The model receives the list of available tools with descriptions and schemas, and then during conversation it can invoke them as needed. The human-in-the-loop can approve or deny tool calls depending on the client's configuration.
MCP servers expose three types of capabilities:
Tools are executable operations. They are functions the AI model can invoke to perform actions. Examples: querying a database, creating a GitHub issue, sending a Slack message, running a shell command. Each tool has a name, description, and a JSON Schema defining its input parameters. Tools are the most commonly used MCP primitive.
Resources are data sources the AI can read. They provide context rather than performing actions. Examples: the contents of a file, a database table schema, documentation, configuration files. Resources have URIs (like file:///path/to/file or postgres://db/schema) and return content in text or binary format. Resources can be static or dynamic.
Prompts are reusable prompt templates that the server provides. They let the server offer pre-built workflows or interaction patterns. Examples: a "code review" prompt that takes a file path and returns a structured review template, or a "SQL query builder" prompt that guides the AI through building a safe database query. Prompts are less commonly used than tools and resources but powerful for standardizing workflows.
Tools perform actions (write, create, delete, send). Resources provide data (read, list, describe). Prompts provide templates (guide, structure, format). A well-designed MCP server uses the right primitive for each capability. Do not expose a read-only operation as a tool when it should be a resource.
| Feature | MCP | OpenAI Function Calling | ChatGPT Plugins (deprecated) |
|---|---|---|---|
| Open standard | Yes | No (OpenAI-specific) | No (deprecated) |
| Provider-agnostic | Yes | No | No |
| Dynamic tool discovery | Yes | No (defined at call time) | Limited |
| Resources (read context) | Yes | No | No |
| Local + remote support | Yes (stdio + SSE) | Remote only | Remote only |
| Community ecosystem | Thousands of servers | N/A | Deprecated |
| Human-in-the-loop | Built-in approval flow | Application-dependent | No |
| Prompt templates | Yes | No | No |
The short version: MCP is the open, universal standard. Function calling is a provider-specific feature. MCP servers can be used by any MCP-compatible client regardless of which AI model it uses, making it the clear choice for building reusable tool integrations.
What it does: Provides controlled read and write access to the local filesystem. The AI can read files, list directories, create and edit files, and search for content — all within configured permission boundaries.
Why it matters: This is the foundation for any coding-related AI workflow. Nearly every agentic coding tool uses a filesystem MCP server internally.
What it does: Full GitHub integration — create issues, manage PRs, search repositories, read code, manage branches, and interact with GitHub Actions. Supports both personal and organization repos.
Why it matters: Enables AI agents to participate in the full software development lifecycle directly through GitHub.
What it does: Connects AI models to Postgres databases. Read-only queries by default (safety first), with optional write support. Exposes table schemas as resources so the AI understands your data model.
Why it matters: Turns your AI assistant into a data analyst that understands your actual production data.
What it does: Read and send Slack messages, search channels, manage threads, and react to messages. Respects Slack's permission model and rate limits.
Why it matters: Lets AI agents participate in team communication, summarize threads, and respond to questions using context from other connected tools.
What it does: Gives AI models web search capabilities through the Brave Search API. Search the web, get results with snippets, and access current information beyond the model's training data.
Why it matters: Bridges the gap between the AI's static knowledge and real-time web information.
What it does: Manage Docker containers, images, networks, and volumes. Start and stop containers, view logs, execute commands inside containers, and manage Docker Compose stacks.
Why it matters: Enables AI-driven DevOps workflows and infrastructure management.
Our digital toolkit includes MCP server templates, prompt libraries, and workflow guides for AI development.
Get the Toolkit Read More GuidesBuilding an MCP server is surprisingly straightforward. The official SDKs handle the protocol plumbing, so you focus on defining your tools and implementing their logic. Here is the process:
pip install mcp for Python or npm install @modelcontextprotocol/sdk for TypeScript.Here is a complete, minimal MCP server in Python that provides a weather lookup tool:
# weather_server.py
from mcp.server import Server
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
import httpx
import json
server = Server("weather-server")
@server.list_tools()
async def list_tools():
return [
Tool(
name="get_weather",
description="Get current weather for a city",
inputSchema={
"type": "object",
"properties": {
"city": {
"type": "string",
"description": "City name, e.g. San Francisco"
}
},
"required": ["city"]
}
)
]
@server.call_tool()
async def call_tool(name: str, arguments: dict):
if name == "get_weather":
city = arguments["city"]
async with httpx.AsyncClient() as client:
resp = await client.get(
f"https://wttr.in/{city}?format=j1"
)
data = resp.json()
current = data["current_condition"][0]
result = {
"city": city,
"temp_c": current["temp_C"],
"temp_f": current["temp_F"],
"condition": current["weatherDesc"][0]["value"],
"humidity": current["humidity"],
"wind_mph": current["windspeedMiles"]
}
return [TextContent(
type="text",
text=json.dumps(result, indent=2)
)]
async def main():
async with stdio_server() as (read, write):
await server.run(read, write)
if __name__ == "__main__":
import asyncio
asyncio.run(main())
That is a complete, working MCP server in under 50 lines. Install it with pip install mcp httpx and connect it to Claude Code by adding it to your claude_desktop_config.json or your project's .claude/settings.json.
The same weather server in TypeScript:
// weather-server.ts
import { Server } from "@modelcontextprotocol/sdk/server/index.js";
import { StdioServerTransport } from
"@modelcontextprotocol/sdk/server/stdio.js";
import {
CallToolRequestSchema,
ListToolsRequestSchema
} from "@modelcontextprotocol/sdk/types.js";
const server = new Server(
{ name: "weather-server", version: "1.0.0" },
{ capabilities: { tools: {} } }
);
server.setRequestHandler(ListToolsRequestSchema, async () => ({
tools: [{
name: "get_weather",
description: "Get current weather for a city",
inputSchema: {
type: "object",
properties: {
city: {
type: "string",
description: "City name, e.g. San Francisco"
}
},
required: ["city"]
}
}]
}));
server.setRequestHandler(CallToolRequestSchema, async (req) => {
if (req.params.name === "get_weather") {
const city = req.params.arguments?.city as string;
const resp = await fetch(
`https://wttr.in/${city}?format=j1`
);
const data = await resp.json();
const current = data.current_condition[0];
return {
content: [{
type: "text",
text: JSON.stringify({
city,
temp_c: current.temp_C,
temp_f: current.temp_F,
condition: current.weatherDesc[0].value,
humidity: current.humidity,
wind_mph: current.windspeedMiles
}, null, 2)
}]
};
}
throw new Error(`Unknown tool: ${req.params.name}`);
});
const transport = new StdioServerTransport();
await server.connect(transport);
Anthropic provides a project scaffolding tool: npx create-mcp-server for TypeScript or uvx create-mcp-server for Python. This generates a complete project structure with configuration, type definitions, and example tools. It is the fastest way to start a new MCP server project.
Testing MCP servers is critical because bugs in a tool that an AI model uses can lead to incorrect actions with real consequences. Here is a testing strategy:
MCP Inspector. The official testing tool. Run npx @modelcontextprotocol/inspector and point it at your server. You can browse tools, resources, and prompts, test individual tool calls with custom arguments, and see the raw JSON-RPC messages. This is your primary debugging tool during development.
Unit tests for tool handlers. Test your tool handler functions in isolation, independent of the MCP protocol. Pass in test arguments, verify the output. This catches logic bugs before they reach the protocol layer.
Integration tests with a test client. The MCP SDKs provide client libraries you can use in tests. Write a test that connects to your server, calls a tool, and asserts on the response. This verifies the full protocol round-trip.
Error handling tests. Test what happens when tools receive invalid arguments, when external APIs are down, when permissions are insufficient. A robust MCP server returns clear error messages rather than crashing, because the AI model needs to understand what went wrong and try a different approach.
Logging. Add structured logging to your server. Log every tool call with its arguments and result. Log errors with full context. When something goes wrong in production, logs are your primary debugging resource. Use stderr for logging (stdout is reserved for MCP protocol messages when using stdio transport).
MCP servers are a security surface that deserves careful attention. An MCP server that connects to your database or filesystem is granting an AI model access to sensitive systems. Here are the key security principles:
Every MCP server should expose the minimum capabilities needed. If your use case only requires reading database records, do not expose write operations. If the AI only needs access to one directory, do not expose the entire filesystem. Define narrow permissions and stick to them.
Input validation. Validate every argument that comes into a tool handler. The AI model can send unexpected inputs, and downstream systems (databases, APIs, shell commands) can be vulnerable to injection attacks. Use the JSON Schema input validation that MCP provides, and add application-level validation on top.
Authentication and authorization. For remote MCP servers (SSE transport), implement proper authentication. Use API keys, OAuth tokens, or mTLS to verify that only authorized clients can connect. Do not expose MCP servers to the public internet without authentication.
Sandboxing. Run MCP servers with limited system permissions. Use containers, restricted file system access, and network policies to limit the blast radius if something goes wrong. A filesystem MCP server should run in a sandbox that can only access designated directories.
Audit logging. Log every tool invocation with the client identity, timestamp, arguments, and result. This creates an audit trail that is essential for debugging, compliance, and security incident investigation.
Rate limiting. Prevent runaway AI agents from overwhelming your systems. Implement rate limits on tool calls, especially for operations that are expensive or have side effects.
Our digital toolkit includes MCP server templates, configuration examples, and step-by-step project guides.
Get the Toolkit Free Developer ToolsHere are the essential resources for MCP development in 2026:
The complete protocol specification at spec.modelcontextprotocol.io. The authoritative reference for the protocol, message formats, and capabilities.
Tutorials, guides, and API reference at modelcontextprotocol.io. Start here if you are new to MCP.
Browse thousands of community-built MCP servers. Find pre-built integrations for most popular services before building your own.
The official testing and debugging tool. Essential for development. Install with npx @modelcontextprotocol/inspector.
pip install mcp — The official Python SDK with async support, type hints, and decorators for clean server definitions.
npm install @modelcontextprotocol/sdk — The official TypeScript SDK with full type safety and both stdio and SSE transport support.
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MCP is still early, and the roadmap includes several significant developments:
Streamable HTTP transport. The next generation of the remote transport protocol, replacing SSE with a more efficient bidirectional streaming mechanism. This will enable better performance for high-throughput MCP servers and support for server-initiated notifications.
OAuth 2.0 integration. Built-in authentication support in the protocol itself, making it easier to build secure remote MCP servers without rolling your own auth layer.
Agent-to-agent communication. MCP is evolving to support not just human-AI-tool interactions but also AI-agent-to-AI-agent communication. One agent's MCP server could expose capabilities that other agents consume, enabling hierarchical agent architectures.
Composable servers. The ability to chain MCP servers together, where one server's output feeds into another server's input. This enables complex workflows without requiring monolithic servers that do everything.
Enterprise features. Better support for auditing, compliance, role-based access control, and centralized server management. As MCP moves into enterprise environments, these features become essential.
The trajectory is clear: MCP is becoming the standard protocol for AI-tool integration. Learning it now puts you ahead of the curve. The developers who understand MCP deeply — who can build custom servers, debug protocol issues, and architect MCP-based systems — will be among the most valuable engineers in the AI era.
Templates, MCP server starters, prompt libraries, and workflow guides for building with AI. Everything you need to start shipping.
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