Daktela MCP Server for LangChainGive LangChain instant access to 12 tools to Create Contact, Create Ticket, Get Me, and more
LangChain is the leading Python framework for composable LLM applications. Connect Daktela through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
Ask AI about this App Connector for LangChain
The Daktela app connector for LangChain is a standout in the Communication Messaging category — giving your AI agent 12 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with MultiServerMCPClient({
"daktela": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Daktela, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Daktela MCP Server
Connect your Daktela omnichannel contact center to any AI agent and simplify how you coordinate customer support, track communication history, and manage CRM records through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Daktela through native MCP adapters. Connect 12 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- Ticket Lifecycle — Create, list, and query support tickets and cases to ensure customer issues are resolved promptly.
- Omnichannel Activities — Monitor real-time and past activities across calls, emails, and chats within your center.
- CRM Control — List and create contacts and accounts (companies) to maintain an organized customer directory.
- Call & Email History — Retrieve detailed logs of past phone interactions and email threads for audit and reporting.
- Team & Queue Coordination — List configured queues and system users to manage agent distribution effectively.
- Profile Oversight — Fetch your authenticated user profile and verify system configurations directly from the agent.
The Daktela MCP Server exposes 12 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 12 Daktela tools available for LangChain
When LangChain connects to Daktela through Vinkius, your AI agent gets direct access to every tool listed below — spanning omnichannel, contact-center, voip, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Create a new CRM contact
Create a new ticket
Get current user information
Get details of a specific ticket
List CRM accounts
List recent activities in Daktela
List call history
List CRM contacts
List email history
List contact center queues
List support tickets
List Daktela users
Connect Daktela to LangChain via MCP
Follow these steps to wire Daktela into LangChain. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install dependencies
pip install langchain langchain-mcp-adapters langgraph langchain-openaiReplace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenRun the agent
python agent.pyExplore tools
Why Use LangChain with the Daktela MCP Server
LangChain provides unique advantages when paired with Daktela through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Daktela MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Daktela queries for multi-turn workflows
Daktela + LangChain Use Cases
Practical scenarios where LangChain combined with the Daktela MCP Server delivers measurable value.
RAG with live data: combine Daktela tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Daktela, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Daktela tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Daktela tool call, measure latency, and optimize your agent's performance
Example Prompts for Daktela in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Daktela immediately.
"List all active activities in the contact center."
"Create a support ticket: 'Login issue' for contact 'cont_10293'."
"Show me the email history for contact 'cont_5521'."
Troubleshooting Daktela MCP Server with LangChain
Common issues when connecting Daktela to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersDaktela + LangChain FAQ
Common questions about integrating Daktela MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.