MCP Code Examples

pip install mcp

import asyncio
from mcp import ClientSession
from mcp.client.streamable_http import streamablehttp_client

API_KEY = "YOUR_API_KEY"
MCP_URL = "https://api.xobni.ai/mcp/"

async def main():
    headers = {"Authorization": f"Bearer {API_KEY}"}

    async with streamablehttp_client(MCP_URL, headers=headers) as (read, write, _):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List available tools
            tools = await session.list_tools()
            print("Available tools:")
            for t in tools.tools:
                print(f"  - {t.name}: {t.description[:60]}...")

            # Get agent info
            result = await session.call_tool("get_agent_info", {})
            print(f"\nAgent: {result}")

            # Read inbox (latest 5 emails)
            result = await session.call_tool("read_inbox", {"limit": 5})
            print(f"\nInbox: {result}")

            # Send an email
            result = await session.call_tool("send_email", {
                "to": ["friend@example.com"],
                "subject": "Hello from my AI agent!",
                "body_text": "This email was sent via MCP through Xobni.ai."
            })
            print(f"\nSent: {result}")

            # Search emails semantically
            result = await session.call_tool("search_emails", {
                "query": "meeting next week",
                "limit": 5
            })
            print(f"\nSearch results: {result}")

            # Store a document
            result = await session.call_tool("store_document", {
                "collection": "contacts",
                "data": {"name": "Jane Doe", "email": "jane@example.com"},
                "metadata": {"source": "conference"}
            })
            print(f"\nStored: {result}")

            # Ask AI about stored documents (RAG)
            result = await session.call_tool("ask_storage", {
                "question": "Who did we meet at the conference?",
                "collection": "contacts"
            })
            print(f"\nAI Answer: {result}")

            # Calendar: create an event
            result = await session.call_tool("create_calendar_event", {
                "title": "Team Standup",
                "start_time": "2026-03-20T10:00:00",
                "end_time": "2026-03-20T10:30:00",
                "timezone": "America/New_York",
            })
            print(f"\nCreated event: {result}")

            # Schedule an email
            result = await session.call_tool("schedule_email", {
                "send_at": "2026-03-20T09:00:00Z",
                "to": ["recipient@example.com"],
                "subject": "Reminder",
                "body_text": "Don't forget the meeting today!",
            })
            print(f"\nScheduled email: {result}")

asyncio.run(main())

npm install @modelcontextprotocol/sdk

import { Client } from "@modelcontextprotocol/sdk/client/index.js";
import { StreamableHTTPClientTransport } from "@modelcontextprotocol/sdk/client/streamableHttp.js";

const API_KEY = "YOUR_API_KEY";

async function main() {
  const transport = new StreamableHTTPClientTransport(
    new URL("https://api.xobni.ai/mcp/"),
    { requestInit: { headers: { Authorization: `Bearer ${API_KEY}` } } }
  );

  const client = new Client({ name: "my-app", version: "1.0.0" });
  await client.connect(transport);

  // List tools
  const { tools } = await client.listTools();
  console.log("Tools:", tools.map(t => t.name));

  // Read inbox
  const inbox = await client.callTool({
    name: "read_inbox",
    arguments: { limit: 5 }
  });
  console.log("Inbox:", inbox);

  // Search emails
  const search = await client.callTool({
    name: "search_emails",
    arguments: { query: "project update", limit: 3 }
  });
  console.log("Search:", search);

  // Store a document
  const stored = await client.callTool({
    name: "store_document",
    arguments: {
      collection: "notes",
      data: { title: "Meeting Notes", content: "Discussed Q2 roadmap" }
    }
  });
  console.log("Stored:", stored);

  // Ask AI about stored documents (RAG)
  const answer = await client.callTool({
    name: "ask_storage",
    arguments: { question: "What was discussed in meetings?" }
  });
  console.log("AI Answer:", answer);

  // Calendar: create an event
  const event = await client.callTool({
    name: "create_calendar_event",
    arguments: {
      title: "Team Standup",
      start_time: "2026-03-20T10:00:00",
      end_time: "2026-03-20T10:30:00",
      timezone: "America/New_York",
    }
  });
  console.log("Created event:", event);

  // Schedule an email
  const scheduled = await client.callTool({
    name: "schedule_email",
    arguments: {
      send_at: "2026-03-20T09:00:00Z",
      to: ["recipient@example.com"],
      subject: "Reminder",
      body_text: "Don't forget the meeting today!",
    }
  });
  console.log("Scheduled email:", scheduled);

  await client.close();
}

main();