Connectto Airtable, DocuSign MCP Servers
Create powerful AI workflows by connecting multiple MCP servers including Airtable, DocuSign for enhanced automation capabilities in Klavis AI.
Airtable
featured
Airtable is a cloud-based database and spreadsheet platform that combines the flexibility of a spreadsheet with the power of a database, enabling teams to organize, track, and collaborate on projects with customizable views and powerful automation features
Available Tools:
- airtable_list_bases_info
- airtable_list_tables_info
- airtable_create_table
- +8 more tools
DocuSign
featured
DocuSign is an electronic signature and agreement cloud platform. Send documents for signature, track status, manage templates, and automate agreement workflows via OpenAPI integration
Quick Setup Guide
Follow these steps to connect your AI agents to these MCP servers
1
Create Your Account
Sign up for KlavisAI to access our MCP server management platform.
2
Configure Connections
Add your desired MCP servers to your AI client and configure authentication settings.
3
Test & Deploy
Verify your connections work correctly and start using your enhanced AI capabilities.
Integrate in minutes, Scale to millions
View Documentationimport os
import json
from anthropic import Anthropic
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat
# Initialize clients
anthropic_client = Anthropic(api_key=os.getenv("ANTHROPIC_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
# Constants
CLAUDE_MODEL = "claude-3-5-sonnet-20241022"
user_message = "Your message here"
airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AIRTABLE,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
docusign_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOCUSIGN,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get tools from all MCP servers
airtable_tools = klavis_client.mcp_server.list_tools(
server_url=airtable_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.ANTHROPIC,
)
docusign_tools = klavis_client.mcp_server.list_tools(
server_url=docusign_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.ANTHROPIC,
)
# Combine all tools
all_tools = []
all_tools.extend(airtable_tools.tools)
all_tools.extend(docusign_tools.tools)
messages = [
{"role": "user", "content": user_message}
]
response = anthropic_client.messages.create(
model=CLAUDE_MODEL,
max_tokens=4000,
messages=messages,
tools=all_tools
)Code Examples for openai
import json
import os
from openai import OpenAI
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat
# Initialize clients
openai_client = OpenAI(api_key=os.getenv("OPENAI_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
# Constants
OPENAI_MODEL = "gpt-4o-mini"
airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AIRTABLE,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
docusign_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOCUSIGN,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get tools from all MCP servers
airtable_tools = klavis_client.mcp_server.list_tools(
server_url=airtable_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
docusign_tools = klavis_client.mcp_server.list_tools(
server_url=docusign_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
# Combine all tools
all_tools = []
all_tools.extend(airtable_tools)
all_tools.extend(docusign_tools)
messages = [
{"role": "system", "content": "You are a helpful assistant."},
{"role": "user", "content": user_message}
]
response = openai_client.chat.completions.create(
model=OPENAI_MODEL,
messages=messages,
tools=all_tools if all_tools else None
)import OpenAI from 'openai';
import { KlavisClient, Klavis } from 'klavis';
// Initialize clients
const openai = new OpenAI({ apiKey: process.env.OPENAI_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });
const OPENAI_MODEL = "gpt-4o-mini";
const userMessage = "Your query here";
const airtableMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.Airtable,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
const docusignMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.DocuSign,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
// Get tools from all MCP servers
const airtableTools = await klavisClient.mcpServer.listTools({
serverUrl: airtableMcpInstance.serverUrl,
connectionType: Klavis.ConnectionType.StreamableHttp,
format: Klavis.ToolFormat.Openai,
});
const docusignTools = await klavisClient.mcpServer.listTools({
serverUrl: docusignMcpInstance.serverUrl,
connectionType: Klavis.ConnectionType.StreamableHttp,
format: Klavis.ToolFormat.Openai,
});
// Combine all tools
const allTools = [
...airtableTools,
...docusignTools
];
const response = await openai.chat.completions.create({
model: OPENAI_MODEL,
messages: [
{ role: 'system', content: 'You are a helpful assistant.' },
{ role: 'user', content: userMessage }
],
tools: allTools,
});Code Examples for gemini
import os
from google import genai
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat
# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
client = genai.Client(api_key=os.getenv("GOOGLE_API_KEY"))
user_message = "Your query here"
airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AIRTABLE,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
docusign_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOCUSIGN,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get tools from all MCP servers
airtable_tools = klavis_client.mcp_server.list_tools(
server_url=airtable_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.GEMINI,
)
docusign_tools = klavis_client.mcp_server.list_tools(
server_url=docusign_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.GEMINI,
)
# Combine all tools
all_tools = []
all_tools.extend(airtable_tools.tools)
all_tools.extend(docusign_tools.tools)
response = client.models.generate_content(
model="gemini-2.5-flash",
contents=user_message,
config=genai.types.GenerateContentConfig(
tools=all_tools,
),
)import { GoogleGenAI } from '@google/genai';
import { KlavisClient, Klavis } from 'klavis';
// Initialize clients
const ai = new GoogleGenAI({ apiKey: process.env.GOOGLE_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });
const userMessage = "Your query here";
const airtableMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.Airtable,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
const docusignMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.DocuSign,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
// Get tools from all MCP servers
const airtableTools = await klavisClient.mcpServer.listTools({
serverUrl: airtableMcpInstance.serverUrl,
connectionType: Klavis.ConnectionType.StreamableHttp,
format: Klavis.ToolFormat.Gemini,
});
const docusignTools = await klavisClient.mcpServer.listTools({
serverUrl: docusignMcpInstance.serverUrl,
connectionType: Klavis.ConnectionType.StreamableHttp,
format: Klavis.ToolFormat.Gemini,
});
// Combine all tools
const allTools = [
...airtableTools.tools,
...docusignTools.tools
];
const response = await ai.models.generateContent({
model: "gemini-2.5-flash",
contents: userMessage,
tools: allTools,
});Code Examples for langchain
import os
import asyncio
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType
from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI
# Initialize clients
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
llm = ChatOpenAI(model="gpt-4o-mini", api_key=os.getenv("OPENAI_API_KEY"))
airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AIRTABLE,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
docusign_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOCUSIGN,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
mcp_client = MultiServerMCPClient({
"airtable": {
"transport": "streamable_http",
"url": airtable_mcp_instance.server_url
},
"docusign": {
"transport": "streamable_http",
"url": docusign_mcp_instance.server_url
}
})
tools = asyncio.run(mcp_client.get_tools())
agent = create_react_agent(
model=llm,
tools=tools,
)
response = asyncio.run(agent.ainvoke({
"messages": [{"role": "user", "content": "Your query here"}]
}))import { KlavisClient, Klavis } from 'klavis';
import { MultiServerMCPClient } from "@langchain/mcp-adapters";
import { ChatOpenAI } from "@langchain/openai";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
// Initialize clients
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });
const llm = new ChatOpenAI({ model: "gpt-4o-mini", apiKey: process.env.OPENAI_API_KEY });
const airtableMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.Airtable,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
const docusignMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.DocuSign,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
const mcpClient = new MultiServerMCPClient({
"airtable": {
transport: "streamable_http",
url: airtableMcpInstance.serverUrl
},
"docusign": {
transport: "streamable_http",
url: docusignMcpInstance.serverUrl
}
});
const tools = await mcpClient.getTools();
const agent = createReactAgent({
llm: llm,
tools: tools,
});
const response = await agent.invoke({
messages: [{ role: "user", content: "Your query here" }]
});Code Examples for llamaindex
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"))
airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AIRTABLE,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
docusign_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOCUSIGN,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
airtable_tools = await aget_tools_from_mcp_url(
airtable_mcp_instance.server_url,
client=BasicMCPClient(airtable_mcp_instance.server_url)
)
docusign_tools = await aget_tools_from_mcp_url(
docusign_mcp_instance.server_url,
client=BasicMCPClient(docusign_mcp_instance.server_url)
)
airtable_agent = FunctionAgent(
name="airtable_agent",
tools=airtable_tools,
llm=llm,
)
docusign_agent = FunctionAgent(
name="docusign_agent",
tools=docusign_tools,
llm=llm,
)
workflow = AgentWorkflow(
agents=[airtable_agent, docusign_agent],
root_agent="airtable_agent",
)import { KlavisClient, Klavis } from 'klavis';
import { mcp } from "@llamaindex/tools";
import { agent, multiAgent } from "@llamaindex/workflow";
import { openai } from "@llamaindex/llm";
// Initialize clients
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });
const airtableMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.Airtable,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
const docusignMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.DocuSign,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
// Create MCP server connections
const airtableServer = mcp({
url: airtableMcpInstance.serverUrl,
verbose: true,
});
const docusignServer = mcp({
url: docusignMcpInstance.serverUrl,
verbose: true,
});
// Get tools from MCP servers
const airtableTools = await airtableServer.tools();
const docusignTools = await docusignServer.tools();
// Create specialized agents
const airtableAgent = agent({
name: "airtable_agent",
llm: openai({ model: "gpt-4o" }),
tools: airtableTools,
});
const docusignAgent = agent({
name: "docusign_agent",
llm: openai({ model: "gpt-4o" }),
tools: docusignTools,
});
// Create multi-agent workflow
const agents = multiAgent({
agents: [airtableAgent, docusignAgent],
rootAgent: airtableAgent,
});Code Examples for crewai
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"))
airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AIRTABLE,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
docusign_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOCUSIGN,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Initialize MCP tools for each server
airtable_tools = MCPServerAdapter(airtable_mcp_instance.server_params)
docusign_tools = MCPServerAdapter(docusign_mcp_instance.server_params)
# Create specialized agents for each service
airtable_agent = Agent(
role="Airtable Specialist",
goal="Handle all Airtable related tasks and data processing",
backstory="You are an expert in Airtable operations and data analysis",
tools=airtable_tools,
reasoning=True,
verbose=False
)
docusign_agent = Agent(
role="DocuSign Specialist",
goal="Handle all DocuSign related tasks and data processing",
backstory="You are an expert in DocuSign operations and data analysis",
tools=docusign_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=airtable_agent,
markdown=True
)
# Create multi-agent crew
crew = Crew(
agents=[airtable_agent, docusign_agent],
tasks=[research_task],
verbose=False,
process=Process.sequential
)
result = crew.kickoff()// CrewAI currently only supports Python. Please use the Python example.Code Examples for fireworks-ai
import os
import json
from fireworks.client import Fireworks
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat
# Initialize clients
fireworks_client = Fireworks(api_key=os.getenv("FIREWORKS_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AIRTABLE,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
docusign_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOCUSIGN,
user_id="1234",
platform_name="Klavis",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get all MCP tools
airtable_tools = klavis_client.mcp_server.list_tools(
server_url=airtable_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
docusign_tools = klavis_client.mcp_server.list_tools(
server_url=docusign_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
# Combine all tools
all_tools = []
all_tools.extend(airtable_tools.tools)
all_tools.extend(docusign_tools.tools)
messages = [
{"role": "system", "content": "You are a helpful assistant with access to multiple data sources."},
{"role": "user", "content": user_message}
]
response = fireworks_client.chat.completions.create(
model="accounts/fireworks/models/llama-v3p1-70b-instruct",
messages=messages,
tools=all_tools
)import Fireworks from 'fireworks-ai';
import { KlavisClient, Klavis } from 'klavis';
// Initialize clients
const fireworks = new Fireworks({ apiKey: process.env.FIREWORKS_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });
const airtableMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.Airtable,
userId: "1234",
platformName: "Klavis",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
const docusignMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.DocuSign,
userId: "1234",
platformName: "Klavis",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
// Get all MCP tools
const airtableTools = await klavisClient.mcpServer.listTools({
serverUrl: airtableMcpInstance.serverUrl,
connectionType: Klavis.ConnectionType.StreamableHttp,
format: Klavis.ToolFormat.Openai,
});
const docusignTools = await klavisClient.mcpServer.listTools({
serverUrl: docusignMcpInstance.serverUrl,
connectionType: Klavis.ConnectionType.StreamableHttp,
format: Klavis.ToolFormat.Openai,
});
// Combine all tools
const allTools = [
...airtableTools.tools,
...docusignTools.tools
];
const response = await fireworks.chat.completions.create({
model: "accounts/fireworks/models/llama-v3p1-70b-instruct",
messages: [
{ role: "system", content: "You are a helpful assistant with access to multiple data sources." },
{ role: "user", content: userMessage }
],
tools: allTools,
});Code Examples for together-ai
import os
import json
from together import Together
from klavis import Klavis
from klavis.types import McpServerName, ConnectionType, ToolFormat
# Initialize clients
together_client = Together(api_key=os.getenv("TOGETHER_API_KEY"))
klavis_client = Klavis(api_key=os.getenv("KLAVIS_API_KEY"))
airtable_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.AIRTABLE,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
docusign_mcp_instance = klavis_client.mcp_server.create_server_instance(
server_name=McpServerName.DOCUSIGN,
user_id="1234",
connection_type=ConnectionType.STREAMABLE_HTTP,
)
# Get all MCP tools
airtable_tools = klavis_client.mcp_server.list_tools(
server_url=airtable_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
docusign_tools = klavis_client.mcp_server.list_tools(
server_url=docusign_mcp_instance.server_url,
connection_type=ConnectionType.STREAMABLE_HTTP,
format=ToolFormat.OPENAI,
)
# Combine all tools
all_tools = []
all_tools.extend(airtable_tools.tools)
all_tools.extend(docusign_tools.tools)
messages = [
{"role": "system", "content": "You are a helpful AI assistant with access to multiple data sources."},
{"role": "user", "content": user_message}
]
response = together_client.chat.completions.create(
model="meta-llama/Llama-2-70b-chat-hf",
messages=messages,
tools=all_tools
)import Together from 'together-ai';
import { KlavisClient, Klavis } from 'klavis';
// Initialize clients
const togetherClient = new Together({ apiKey: process.env.TOGETHER_API_KEY });
const klavisClient = new KlavisClient({ apiKey: process.env.KLAVIS_API_KEY });
const airtableMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.Airtable,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
const docusignMcpInstance = await klavisClient.mcpServer.createServerInstance({
serverName: Klavis.McpServerName.DocuSign,
userId: "1234",
connectionType: Klavis.ConnectionType.StreamableHttp,
});
// Get all MCP tools
const airtableTools = await klavisClient.mcpServer.listTools({
serverUrl: airtableMcpInstance.serverUrl,
connectionType: Klavis.ConnectionType.StreamableHttp,
format: Klavis.ToolFormat.Openai,
});
const docusignTools = await klavisClient.mcpServer.listTools({
serverUrl: docusignMcpInstance.serverUrl,
connectionType: Klavis.ConnectionType.StreamableHttp,
format: Klavis.ToolFormat.Openai,
});
// Combine all tools
const allTools = [
...airtableTools.tools,
...docusignTools.tools
];
const response = await togetherClient.chat.completions.create({
model: "meta-llama/Llama-2-70b-chat-hf",
messages: [
{ role: "system", content: "You are a helpful AI assistant with access to multiple data sources." },
{ role: "user", content: userMessage }
],
tools: allTools,
});Frequently Asked Questions
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