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Use CrewAI when you want separate agents for collection, analysis, and reporting while sharing one Twexapi MCP connection.

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Prerequisites

  • Python 3.10+
  • A Twexapi API key from the dashboard
  • An LLM API key supported by CrewAI

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Install

pip install crewai python-dotenv

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Full example

import os
from pathlib import Path

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

load_dotenv()

twexapi_mcp = MCPServerHTTP(
    url="https://api.twexapi.io/mcp",
    headers={"x-api-key": os.environ["TWEXAPI_API_KEY"]},
)

researcher = Agent(
    role="X Research Analyst",
    goal="Collect structured public X/Twitter data with Twexapi MCP",
    backstory="A careful analyst who preserves IDs, cursors, and route names.",
    mcps=[twexapi_mcp],
)

task = Task(
    description=(
        "Search recent tweets about AI agents. Use explore first. Return compact JSON "
        "with route_used, tweets[{tweet_id,text,author_username,created_at}], "
        "has_more, next_cursor, and key influencers."
    ),
    expected_output="Compact JSON with tweet rows, pagination state, and influencer notes.",
    agent=researcher,
)

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

result = crew.kickoff()
Path("twexapi-crewai-handoff.json").write_text(str(result), encoding="utf-8")

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Multi-agent crew

Give only the data-collection agents Twexapi access. Downstream agents can work from the handoff JSON. 
analyst = Agent(
    role="Engagement Analyst",
    goal="Find patterns in tweet rows and profile rows",
    backstory="Transforms structured social data into concise findings.",
)

writer = Agent(
    role="Report Writer",
    goal="Write a short executive summary from the analyst output",
    backstory="Creates readable summaries without inventing missing metrics.",
)

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Handoff checklist

  • Preserve tweet_id, user_id, author_username, created_at, and source query.
  • Preserve has_more and next_cursor for every paginated call.
  • Store route_used, method, and path so the workflow can be audited.
  • Require human approval before any read_only: false action.

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Tool filtering

When a CrewAI role should only discover endpoints, limit that agent to explore. Give twexapi_request only to agents that are allowed to make authenticated API calls. 
from crewai.mcp import MCPServerHTTP
from crewai.mcp.filters import create_static_tool_filter

read_only_catalog = MCPServerHTTP(
    url="https://api.twexapi.io/mcp",
    headers={"x-api-key": os.environ["TWEXAPI_API_KEY"]},
    tool_filter=create_static_tool_filter(
        allowed_tool_names=["explore"],
    ),
)
For production research crews, keep write-capable work in a separate task that asks for explicit approval before any read_only: false call.

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Environment variables

.env
TWEXAPI_API_KEY=twexapi_YOUR_KEY_HERE
OPENAI_API_KEY=sk-...
ANTHROPIC_API_KEY=sk-ant-...

import os
from crewai.mcp import MCPServerHTTP

twexapi_mcp = MCPServerHTTP(
    url="https://api.twexapi.io/mcp",
    headers={"x-api-key": os.environ["TWEXAPI_API_KEY"]},
)

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Package versions

PackageVersion
crewai1.0+
mcp1.9+