Documentation Index Fetch the complete documentation index at: https://docs.stackone.com/llms.txt
Use this file to discover all available pages before exploring further.
Overview
CrewAI includes native MCP integration via the mcps parameter on agents, enabling direct connection to StackOne’s MCP server using either simple URL strings or structured configuration objects.
Official Docs
Installation
Or with uv:
Quick Start
Connect to StackOne MCP using structured configuration:
import os
import base64
from crewai import Agent, Crew, Task
from crewai.mcp import MCPServerHTTP
# Configure StackOne account
STACKONE_ACCOUNT_ID = "<account_id>" # Your StackOne account ID
# Encode API key for Basic auth
auth_token = base64.b64encode(
f " { os.getenv( 'STACKONE_API_KEY' ) } :" .encode()
).decode()
# Create agent with StackOne MCP server
data_agent = Agent(
role = "Platform Data Analyst" ,
goal = "Analyze data from connected platforms and provide insights" ,
backstory = "Expert in cross-platform data integration and analysis" ,
mcps = [
MCPServerHTTP(
url = "https://api.stackone.com/mcp" ,
headers = {
"Authorization" : f "Basic { auth_token } " ,
"x-account-id" : STACKONE_ACCOUNT_ID
}
)
]
)
# Create task
analysis_task = Task(
description = "Search recent calls in Gong and summarize key insights" ,
agent = data_agent,
expected_output = "A summary of recent call activities and insights"
)
# Create and run crew
crew = Crew(
agents = [data_agent],
tasks = [analysis_task]
)
result = crew.kickoff()
print (result)
Environment Variables
STACKONE_API_KEY =< stackone_api_key >
Configuration Options
When using MCPServerHTTP, you can configure:
url: The MCP server endpoint
headers: Custom headers for authentication
tool_filter: Optional filter to restrict which tools are available
cache_tools_list: Whether to cache the tool list (default: True)
Next Steps
CrewAI MCP Docs Official CrewAI MCP documentation
StackOne API Learn about StackOne’s Platform API