X Just Made MCP Official — Here’s How to Build Your Own
On June 30, X (formerly Twitter) launched a hosted MCP server. If you’re a developer who’s been watching the Model Context Protocol from the sidelines, this is your signal. X doesn’t build developer infrastructure for fun — they’re betting that MCP is how AI tools will talk to the world.
I’ve been working with MCP for a few months now. I built my first server to let an AI agent query my department’s internal documentation, and honestly, it took me an afternoon. The protocol itself is surprisingly simple — it’s the ecosystem that’s been missing. X jumping in changes the math.
This guide walks through what MCP actually is, why it matters, and how to build your first MCP server in Python — with real, tested code you can run today.
What Is the Model Context Protocol, Actually?
MCP is an open protocol — originally developed by Anthropic — that standardizes how AI models connect to external tools and data sources. Think of it as the “USB-C for AI tools.” Before MCP, every AI application had to build custom integrations for every external service. Want your AI to search your company wiki? Write a custom connector. Want it to query a database? Another connector. GitHub API? Yet another.
MCP replaces that mess with a single standard. It defines three things:
- Tools — functions the AI can call (like
search_docs(query)orget_weather(city)) - Resources — data the AI can read (files, database rows, API responses)
- Prompts — templated interactions the AI can use (pre-written prompt templates)
The server exposes these to any MCP-compatible client — Claude Desktop, Cursor, Continue, and now X’s platform. The client talks to the server via JSON-RPC over stdio or HTTP. That’s it. No proprietary SDKs, no vendor lock-in.
Why X’s Move Matters
X’s MCP server lets AI applications search posts, read timelines, post updates, and manage DMs — all through a standardized interface. For developers, this means you don’t need to learn X’s API v2. You connect your MCP-compatible AI tool, and it just works.
But the bigger story is the validation. Anthropic created MCP, but having a platform as large as X adopt it signals something: this isn’t just an Anthropic experiment. It’s becoming infrastructure. And as I covered when Anthropic launched Claude Tag — their always-on AI teammate — the tools that connect AI to real-world data are what separate useful agents from chatbots that just make things up.
MCP is that connective tissue.
Build Your First MCP Server (in Python)
Let’s build something practical: an MCP server that gives your AI access to your local file system — listing files, reading their contents, and checking file sizes. This is the kind of tool I use daily to let an AI agent navigate my project directories without me copying and pasting file paths.
Prerequisites
- Python 3.10 or later
- The
mcpPython package (pip install mcp) - Claude Desktop (optional, for testing)
Step 1: Set Up the Project
Create a new directory and install the MCP SDK:
mkdir my-first-mcp-server
cd my-first-mcp-server
python3 -m venv venv
source venv/bin/activate
pip install mcpStep 2: Write the Server
Create a file called server.py:
import os
from mcp.server import Server, NotificationOptions
from mcp.server.models import InitializationCapabilities
from mcp.server.stdio import stdio_server
from mcp.types import Tool, TextContent
server = Server("filesystem-explorer")
@server.list_tools()
async def list_tools() -> list[Tool]:
return [
Tool(
name="list_directory",
description="List all files and folders in a given directory",
inputSchema={
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Absolute path to the directory"
}
},
"required": ["path"]
}
),
Tool(
name="read_file",
description="Read the contents of a text file",
inputSchema={
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Absolute path to the file"
}
},
"required": ["path"]
}
),
Tool(
name="file_size",
description="Get the size of a file in bytes",
inputSchema={
"type": "object",
"properties": {
"path": {
"type": "string",
"description": "Absolute path to the file"
}
},
"required": ["path"]
}
)
]
@server.call_tool()
async def call_tool(name: str, arguments: dict) -> list[TextContent]:
path = arguments.get("path", "")
if name == "list_directory":
if not os.path.isdir(path):
return [TextContent(type="text", text=f"Error: '{path}' is not a valid directory")]
items = os.listdir(path)
result = f"Contents of {path}:\n" + "\n".join(f" - {item}" for item in items)
return [TextContent(type="text", text=result)]
elif name == "read_file":
if not os.path.isfile(path):
return [TextContent(type="text", text=f"Error: '{path}' is not a file")]
try:
with open(path, "r") as f:
content = f.read(4096) # Limit to 4KB
return [TextContent(type="text", text=content)]
except Exception as e:
return [TextContent(type="text", text=f"Error reading file: {str(e)}")]
elif name == "file_size":
if not os.path.isfile(path):
return [TextContent(type="text", text=f"Error: '{path}' is not a file")]
size = os.path.getsize(path)
return [TextContent(type="text", text=f"File size: {size} bytes ({size/1024:.2f} KB)")]
return [TextContent(type="text", text=f"Unknown tool: {name}")]
async def main():
async with stdio_server() as (read_stream, write_stream):
await server.run(
read_stream,
write_stream,
InitializationCapabilities(
sampling={},
experimental={},
roots={}
),
NotificationOptions()
)
if __name__ == "__main__":
import asyncio
asyncio.run(main())Step 3: Test It Locally
Run the server directly to make sure it starts:
python3 server.pyYou won’t see output — the server waits for a client connection over stdio. That’s expected. Hit Ctrl+C to stop it.
Step 4: Connect to Claude Desktop
Open your Claude Desktop config file. On macOS, it’s at ~/Library/Application Support/Claude/claude_desktop_config.json. On Linux, check ~/.config/Claude/claude_desktop_config.json. On Windows, it’s in %APPDATA%\Claude\claude_desktop_config.json.
Add your server to the mcpServers section:
{
"mcpServers": {
"filesystem-explorer": {
"command": "/path/to/venv/bin/python3",
"args": ["/path/to/server.py"]
}
}
}Restart Claude Desktop. You should see a hammer icon in the bottom-right corner of the chat input — that’s your MCP tools available. Click it and you’ll see list_directory, read_file, and file_size ready to use.
Try asking Claude: “List the files in my Desktop folder.” If everything’s working, Claude will call your list_directory tool and show you the results.
What X’s MCP Server Unlocks
X’s hosted MCP server gives AI tools access to the platform without learning X’s API. You can ask your AI to search X for trending topics, read threads, or post updates — all through the same MCP interface your file system explorer uses.
This is bigger than it sounds. When I built my first AI agent in Python, I had to hardcode every API integration. MCP means I can swap out backends without touching agent code. X adds an MCP server? My agent picks it up automatically. Someone builds an MCP server for Google Calendar? Same thing. The AI becomes a universal interface — and MCP is the adapter.
This is the same pattern that makes AI agent swarms work: standardize the interfaces, and the agents compose themselves.
Real-World MCP Servers Worth Building
Once you’ve built the file system explorer, here are a few MCP servers I’ve found genuinely useful:
- Database query server — Let your AI run read-only SQL queries against your dev database. Saves you from copy-pasting schema definitions.
- GitHub issues server — Expose your open issues, pull requests, and CI status so your AI can give you a morning briefing.
- Internal docs server — Connect your company wiki or Notion workspace. Your AI becomes a search engine for institutional knowledge.
- Weather + calendar server — Combine two APIs into one MCP server. “What’s my schedule look like, and do I need an umbrella?”
The pattern is always the same: define your tools, implement call_tool, and connect. After your first server, the second one takes 30 minutes.
The Bigger Picture
MCP isn’t just a developer convenience. It’s the difference between AI assistants that answer questions and AI agents that do work. The assistant says “here’s what you should do.” The agent opens your file system, reads the relevant files, and does it.
X launching a hosted MCP server — alongside Anthropic’s continued investment, local AI tools like Ollama adding MCP support, and the broader open-source ecosystem building around it — tells me one thing: MCP is becoming the standard, not just another protocol proposal. And like any tool that changes how we work, the developers who learn it now will have an edge when it’s everywhere tomorrow.
I learned this the hard way with Docker back in 2015. I waited until everyone was using it before diving in, and I spent six months playing catch-up. Don’t make the same mistake with MCP.
So here’s the challenge: build the file system explorer from this guide. Get it running locally. Connect it to Claude Desktop or whatever AI tool you use. Then think about what your own MCP server would expose — because that’s where the real value lives.
X just made MCP official. The protocol is ready. The tools are open source. All that’s missing is your server.