There's a problem that every AI engineer has quietly dealt with for two years: tool integrations are a mess. You build an integration to give your LLM access to GitHub. Then you rebuild it for a different model. Then you rebuild it again for a different framework. Every AI system reinvents its own tool format, its own context management, its own resource access patterns.
Anthropic's Model Context Protocol (MCP) is attempting to fix this — and it might be the most important AI infrastructure development that most engineers aren't paying attention to.
The Problem MCP Solves
Before MCP, connecting an LLM to external tools required:
- Writing a custom tool definition for each model's format (OpenAI, Anthropic, and Gemini all have different schemas)
- Managing context injection manually
- Building custom servers for each integration
- Rebuilding everything when you switched models
This created a fragmented ecosystem where integrations were tightly coupled to specific models and frameworks. The same GitHub integration would be built three different ways for three different AI products.
MCP proposes a standardized protocol for how AI models and tools communicate — a universal interface that works across models, clients, and deployment environments.
What MCP Actually Is
At its core, MCP is a JSON-RPC protocol that defines how "hosts" (AI applications like Claude Desktop, Cursor, or your custom app) communicate with "servers" (programs that expose tools, resources, and prompts).
The key abstractions:
- Tools — actions the model can invoke (run a query, create a file, send an email)
- Resources — data the model can read (files, database records, API responses)
- Prompts — reusable templates that can be dynamically constructed
MCP servers are simple processes that implement this protocol — they can be written in any language and run locally or remotely.
Building Your First MCP Server
Here's a minimal MCP server in Python using the official SDK:
from mcp.server import Server
from mcp.server.models import InitializationOptions
import mcp.server.stdio
import mcp.types as types
# Initialize the server
server = Server("my-ai-tools")
@server.list_tools()
async def list_tools() -> list[types.Tool]:
return [
types.Tool(
name="search_docs",
description="Search internal documentation for relevant information",
inputSchema={
"type": "object",
"properties": {
"query": {
"type": "string",
"description": "The search query"
}
},
"required": ["query"]
}
)
]
@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[types.TextContent]:
if name == "search_docs":
query = arguments.get("query", "")
# Your actual search logic here
results = await search_internal_docs(query)
return [types.TextContent(type="text", text=results)]
# Run as stdio server
async def main():
async with mcp.server.stdio.stdio_server() as (read_stream, write_stream):
await server.run(read_stream, write_stream, InitializationOptions())
Once this server is running, any MCP-compatible client — Claude Desktop, Cursor, or your own application — can discover and use these tools.
The Ecosystem Growing Around MCP
Since Anthropic published the MCP spec, the community has built hundreds of servers:
- File system access — read/write local files
- GitHub — search repos, create issues, review PRs
- Postgres/SQLite — execute queries, explore schemas
- Brave Search — real-time web search
- Slack — read messages, send notifications
- Browser automation — control web browsers
- Obsidian — read and write notes
The official MCP server registry is growing weekly. This is the network effect that makes MCP compelling — build one server, work with all clients.
MCP vs Function Calling
The obvious question: how does MCP differ from OpenAI function calling?
Function calling is a feature of model APIs — you define functions, the model decides when to call them, and you execute them yourself. It's tightly coupled to the inference call.
MCP is a separate infrastructure layer — a protocol for persistent server processes that expose capabilities to any AI application. It's decoupled from the model API itself.
In practice, MCP-compatible AI applications often use function calling internally when communicating with models — MCP is the transport layer that connects your app to tool servers, not a replacement for function calling.
Should You Build with MCP Today?
MCP adoption in early 2026 is accelerating fast. Claude Desktop, Cursor, and VS Code Copilot all support it. The spec is stable. The tooling is good.
My recommendation:
- If you're building internal AI tooling for your team → build MCP servers. They're immediately useful with Claude Desktop.
- If you're building a product → watch the space. MCP client support in production APIs is still maturing.
- If you're a solo developer → absolutely worth learning. MCP server development is a valuable and differentiated skill right now.
The standardization moment for AI tools is happening. MCP might be the standard that sticks. Worth understanding before everyone else does.