Connectto Postgres, Gong, Calendly MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Postgres, Gong, Calendly for enhanced automation capabilities in Klavis AI.
Postgres
PostgreSQL is a powerful, open source object-relational database system
Available Tools:
- query
Gong
Gong is a revenue intelligence platform that captures and analyzes all revenue-related interactions to help sales teams close more deals. It provides conversation analytics, deal insights, and sales performance tracking through call recordings and transcripts
Available Tools:
- gong_get_transcripts_by_user
- gong_get_extensive_data
- gong_get_call_transcripts
- +2 more tools
Calendly
Manage scheduling and appointments with your agents.
Quick Setup Guide
Follow these steps to connect LlamaIndex to these MCP servers
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
Configure Connections
Add your desired MCP servers to LlamaIndex and configure authentication settings.
Test & Deploy
Verify your connections work correctly and start using your enhanced AI capabilities.
LlamaIndex + KlavisAI Integration Snippets
import os
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType
from llama_index.tools.mcp import (
BasicMCPClient,
get_tools_from_mcp_url,
aget_tools_from_mcp_url,
)
from llama_index.core.agent.workflow import FunctionAgent, AgentWorkflow
# 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,
)
gong_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.GONG,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
calendly_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.CALENDLY,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
postgres_tools = await aget_tools_from_mcp_url(
postgres_mcp_instance.server_url,
client=BasicMCPClient(postgres_mcp_instance.server_url)
)
gong_tools = await aget_tools_from_mcp_url(
gong_mcp_instance.server_url,
client=BasicMCPClient(gong_mcp_instance.server_url)
)
calendly_tools = await aget_tools_from_mcp_url(
calendly_mcp_instance.server_url,
client=BasicMCPClient(calendly_mcp_instance.server_url)
)
postgres_agent = FunctionAgent(
name="postgres_agent",
tools=postgres_tools,
llm=llm,
)
gong_agent = FunctionAgent(
name="gong_agent",
tools=gong_tools,
llm=llm,
)
calendly_agent = FunctionAgent(
name="calendly_agent",
tools=calendly_tools,
llm=llm,
)
workflow = AgentWorkflow(
agents=[postgres_agent, gong_agent, calendly_agent],
root_agent="postgres_agent",
)Frequently Asked Questions
Everything you need to know about connecting to these MCP servers
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