Documentation Index
Fetch the complete documentation index at: https://docs.xquik.com/llms.txt
Use this file to discover all available pages before exploring further.
Build a Microsoft Agent Framework agent that can search tweets, look up users, post tweets, and run extractions - all through Xquik’s MCP server.
Prerequisites
- Python 3.10+
- Xquik API key (
xq_...)
- An LLM API key (OpenAI, Azure OpenAI, or any supported provider)
Install
pip install agent-framework mcp
agent-framework meta-package installs all core and provider sub-packages. The mcp package enables MCPStreamableHTTPTool support.
Full Example
import asyncio
from agent_framework import ChatAgent, MCPStreamableHTTPTool
from agent_framework.openai import OpenAIChatClient
async def main():
mcp_tool = MCPStreamableHTTPTool(
name="xquik",
url="https://xquik.com/mcp",
headers={"x-api-key": "xq_YOUR_KEY_HERE"},
description="X (Twitter) data platform with 120 API endpoints",
)
async with mcp_tool:
agent = ChatAgent(
chat_client=OpenAIChatClient(model_id="gpt-4o"),
name="xquik_agent",
instructions="You help users interact with X (Twitter) via the Xquik API.",
tools=[mcp_tool],
)
response = await agent.run("Search for tweets about AI agents")
print(response)
asyncio.run(main())
explore + xquik) and can call any of the 120 API endpoints.
Streaming Responses
Use run_stream for real-time token streaming:
async with mcp_tool:
agent = ChatAgent(
chat_client=OpenAIChatClient(model_id="gpt-4o"),
name="xquik_agent",
instructions="You help users interact with X (Twitter) via the Xquik API.",
tools=[mcp_tool],
)
async for update in agent.run_stream("What are the trending topics on X right now?"):
if update.text:
print(update.text, end="")
Multi-Agent Orchestration
Use GroupChatBuilder to coordinate specialized agents sharing the same MCP tools:
import asyncio
from agent_framework import ChatAgent, MCPStreamableHTTPTool, GroupChatBuilder
from agent_framework.openai import OpenAIChatClient
async def main():
client = OpenAIChatClient(model_id="gpt-4o")
mcp_tool = MCPStreamableHTTPTool(
name="xquik",
url="https://xquik.com/mcp",
headers={"x-api-key": "xq_YOUR_KEY_HERE"},
description="X (Twitter) data platform",
)
async with mcp_tool:
researcher = ChatAgent(
chat_client=client,
name="researcher",
instructions="Search X for tweets and user profiles. Return raw data.",
tools=[mcp_tool],
)
analyst = ChatAgent(
chat_client=client,
name="analyst",
instructions="Analyze tweet data and identify trends, sentiment, and key influencers.",
)
workflow = (
GroupChatBuilder()
.with_orchestrator(
ChatAgent(
chat_client=client,
name="coordinator",
instructions="Coordinate research tasks. Delegate data collection to the researcher and analysis to the analyst.",
)
)
.participants([researcher, analyst])
.build()
)
result = await workflow.run(
"Find the top 10 most engaged tweets about AI agents and analyze the sentiment."
)
print(result)
asyncio.run(main())
tool_resources for multi-tenant apps where each user has their own Xquik API key:
mcp_tool = MCPStreamableHTTPTool(
name="xquik",
url="https://xquik.com/mcp",
description="X (Twitter) data platform",
)
async with mcp_tool:
agent = ChatAgent(
chat_client=OpenAIChatClient(model_id="gpt-4o"),
name="xquik_agent",
instructions="You help users interact with X (Twitter) via the Xquik API.",
tools=[mcp_tool],
)
response = await agent.run(
"Search for tweets about AI agents",
tool_resources={"xquik": {"headers": {"x-api-key": user_api_key}}},
)
tool_resources are available only for the current run and are not persisted.
Azure OpenAI
Swap OpenAIChatClient for AzureOpenAIChatClient to use Azure-hosted models:
from agent_framework.azure import AzureOpenAIChatClient
client = AzureOpenAIChatClient(
endpoint="https://your-resource.openai.azure.com",
deployment_name="gpt-4o",
api_key="your-azure-key",
)
agent = ChatAgent(
chat_client=client,
name="xquik_agent",
instructions="You help users interact with X (Twitter) via the Xquik API.",
tools=[mcp_tool],
)
Environment Variables
Store your API keys in a .env file instead of hardcoding them:
XQUIK_API_KEY=xq_YOUR_KEY_HERE
OPENAI_API_KEY=sk-...
import os
from dotenv import load_dotenv
from agent_framework import MCPStreamableHTTPTool
load_dotenv()
mcp_tool = MCPStreamableHTTPTool(
name="xquik",
url="https://xquik.com/mcp",
headers={"x-api-key": os.environ["XQUIK_API_KEY"]},
description="X (Twitter) data platform",
)
Package Versions
| Package | Version |
|---|
agent-framework | 1.0.0+ |
agent-framework-core | 1.0.0+ |
mcp | 1.9.2+ |
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