Create powerful collaborative AI workflows by connecting multiple MCP servers including Postgres, Google Docs, GitHub for enhanced multi-agent automation capabilities in Klavis AI.
PostgreSQL is a powerful, open source object-relational database system
Google Docs is a word processor included as part of the free, web-based Google Docs Editors suite
Enhanced GitHub MCP Server
Follow these steps to connect CrewAI to these MCP servers
Sign up for KlavisAI to access our MCP server management platform.
Set up your CrewAI agents with your desired MCP servers tools and configure authentication settings for collaborative workflows.
Test your multi-agent workflows and start using your enhanced collaborative AI capabilities.
import os
from crewai import Agent, Task, Crew, Process
from crewai_tools import MCPServerAdapter
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType
# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
postgres_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.POSTGRES,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
google_docs_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GOOGLE_DOCS,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
github_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GITHUB,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
postgres_tools = MCPServerAdapter(postgres_mcp_instance.server_params)
google_docs_tools = MCPServerAdapter(google_docs_mcp_instance.server_params)
github_tools = MCPServerAdapter(github_mcp_instance.server_params)
# Create specialized agents for each service
postgres_agent = Agent(
role="Postgres Specialist",
goal="Handle all Postgres related tasks and data processing",
backstory="You are an expert in Postgres operations and data analysis",
tools=postgres_tools,
reasoning=True,
verbose=False
)
google_docs_agent = Agent(
role="Google Docs Specialist",
goal="Handle all Google Docs related tasks and data processing",
backstory="You are an expert in Google Docs operations and data analysis",
tools=google_docs_tools,
reasoning=True,
verbose=False
)
github_agent = Agent(
role="GitHub Specialist",
goal="Handle all GitHub related tasks and data processing",
backstory="You are an expert in GitHub operations and data analysis",
tools=github_tools,
reasoning=True,
verbose=False
)
# Define collaborative tasks
research_task = Task(
description="Gather comprehensive data from all available sources",
expected_output="Raw data and initial findings from all services",
agent=postgres_agent,
markdown=True
)
analysis_task = Task(
description="Analyze and synthesize the gathered data",
expected_output="Comprehensive analysis with insights and recommendations",
agent=google_docs_agent,
markdown=True
)
# Create multi-agent crew
multi_agent_crew = Crew(
agents=[postgres_agent, google_docs_agent, github_agent],
tasks=[research_task, analysis_task],
verbose=False,
process=Process.sequential
)
result = multi_agent_crew.kickoff()
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