CrewAI

pip install crewai

from crewai import Agent, Task, Crew, Process
from crewai.mcp import MCPServerHTTP

researcher = Agent(
    role="X Research Analyst",
    goal="Find and analyze trending conversations on X",
    backstory="Expert social media analyst with deep experience in trend analysis",
    mcps=[
        MCPServerHTTP(
            url="https://xquik.com/mcp",
            headers={"x-api-key": "xq_YOUR_KEY_HERE"},
        ),
    ],
)

task = Task(
    description="Search for the latest tweets about AI agents, summarize the top 5 most engaged tweets, and identify key influencers in the conversation.",
    expected_output="A structured report with top tweets, engagement metrics, and influencer list",
    agent=researcher,
)

crew = Crew(
    agents=[researcher],
    tasks=[task],
    process=Process.sequential,
    verbose=True,
)

result = crew.kickoff()
print(result)

from crewai import Agent, Task, Crew, Process
from crewai.mcp import MCPServerHTTP

xquik_mcp = MCPServerHTTP(
    url="https://xquik.com/mcp",
    headers={"x-api-key": "xq_YOUR_KEY_HERE"},
)

researcher = Agent(
    role="X Researcher",
    goal="Gather raw data from X about a given topic",
    backstory="Data collection specialist who finds relevant tweets and profiles",
    mcps=[xquik_mcp],
)

analyst = Agent(
    role="Engagement Analyst",
    goal="Analyze engagement patterns and identify trends",
    backstory="Data analyst who turns raw social data into actionable insights",
    mcps=[xquik_mcp],
)

writer = Agent(
    role="Report Writer",
    goal="Write a concise executive summary from the analysis",
    backstory="Technical writer who creates clear, data-driven reports",
)

research_task = Task(
    description="Search X for tweets about '{topic}' from the last 24 hours. Collect the top 20 by engagement.",
    expected_output="Raw tweet data with engagement metrics",
    agent=researcher,
)

analysis_task = Task(
    description="Analyze the collected tweets. Identify sentiment distribution, peak posting times, and top contributors.",
    expected_output="Structured analysis with charts-ready data",
    agent=analyst,
)

report_task = Task(
    description="Write a 3-paragraph executive summary of the findings.",
    expected_output="Executive summary in markdown format",
    agent=writer,
)

crew = Crew(
    agents=[researcher, analyst, writer],
    tasks=[research_task, analysis_task, report_task],
    process=Process.sequential,
    verbose=True,
)

result = crew.kickoff(inputs={"topic": "AI agents"})
print(result)

from crewai.mcp import MCPServerHTTP
from crewai.mcp.filters import create_static_tool_filter

# Only allow read operations — no posting, liking, or following
researcher = Agent(
    role="Read-Only Researcher",
    goal="Gather data without modifying anything",
    backstory="Researcher with read-only access",
    mcps=[
        MCPServerHTTP(
            url="https://xquik.com/mcp",
            headers={"x-api-key": "xq_YOUR_KEY_HERE"},
            tool_filter=create_static_tool_filter(
                allowed_tool_names=["explore"],
            ),
        ),
    ],
)

import os

xquik_mcp = MCPServerHTTP(
    url="https://xquik.com/mcp",
    headers={"x-api-key": os.environ["XQUIK_API_KEY"]},
)