Quickstart - Using MCPs in AI agents

pip install mcp-agents

npm install @observee/auth

from observee_agents import call_mcpauth_login

# Start authentication flow (optionally provide your own client_id)
response = call_mcpauth_login(
    auth_server="gmail",
    client_id="<client_id (uuid)>"  # Optional: use your own UUID
)
print(f"Visit: {response['url']}")

# After you visit the URL and authenticate, you'll receive:
# {"success":true,"client_id":"<client_id (uuid)>"}

import { callMcpAuthLogin } from "@observee/auth";

// Start authentication flow (optionally provide your own client_id)
const response = await callMcpAuthLogin({
    authServer: "gmail",
    clientId: "<client_id (uuid)>"  // Optional: use your own UUID
});
console.log(`Visit: ${response.url}`);

// After you visit the URL and authenticate, you'll receive:
// {"success":true,"client_id":"<client_id (uuid)>"}

pip install mcp-agents

npm install @observee/agents

from observee_agents import chat_with_tools

# Use the client_id from authentication
result = chat_with_tools(
    message="Search for recent news about AI developments",
    provider="anthropic",
    client_id="<client_id (uuid)>"  # Use the client_id from Step 2
)

print("🤖 AI Response:", result["content"])
print("🔧 Tools Used:", len(result["tool_calls"]))

import { chatWithTools } from "@observee/agents";

// Use the client_id from authentication
const result = await chatWithTools("Search for recent news about AI developments", {
    provider: "anthropic",
    clientId: "<client_id (uuid)>"  // Use the client_id from Step 2
});

console.log("🤖 AI Response:", result.content);

import asyncio
from observee_agents import chat_with_tools_stream

async def stream_example():
    async for chunk in chat_with_tools_stream(
        message="Check my emails and summarize them",
        provider="anthropic"
    ):
        if chunk["type"] == "content":
            print(chunk["content"], end="", flush=True)

asyncio.run(stream_example())

import { chatWithToolsStream } from "@observee/agents";

for await (const chunk of chatWithToolsStream("Check my emails and summarize them", {
    provider: "anthropic"
})) {
    if (chunk.type === "content") {
        process.stdout.write(chunk.content);
    }
}

# First message
result1 = chat_with_tools(
    message="Search for emails about the product launch",
    client_id="<client_id (uuid)>",
    session_id="assistant_001"
)

# Follow-up (remembers context)
result2 = chat_with_tools(
    message="What date was mentioned in those emails?",
    client_id="<client_id (uuid)>",
    session_id="assistant_001"  # Same session = memory
)