Freshchat MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Freshchat through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
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Vinkius supports streamable HTTP and SSE.
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({
"freshchat": {
"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 Freshchat, 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 Freshchat MCP Server
Connect your Freshchat account to any AI agent to automate your customer messaging and conversation management through the Model Context Protocol (MCP). Freshchat is a modern messaging software built for sales and support teams to engage with customers across web, mobile, and social channels. This MCP server enables you to track active chats, send real-time messages, and retrieve detailed user profiles directly through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Freshchat through native MCP adapters. Connect 12 tools via the 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.
Key Features
- Conversation Oversight — List all active chats, fetch detailed conversation metadata, and monitor chat statuses (open, resolved) instantly.
- Real-time Messaging — Post new messages to existing conversations to keep your support workflows moving fast.
- User & Customer Data — Access detailed profile information for chat participants and search for users by email address.
- Support Team Insights — List all support agents and team members to maintain full context of who is online and available.
- Channel & Group Management — Access configured messaging channels and agent groups to understand your routing logic.
- Message History — Retrieve the full message history for any specific conversation ID for audit and reporting.
- Multi-Region Support — Seamlessly connect to your specific Freshchat data center (US, EU, IN, AU).
The Freshchat 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.
How to Connect Freshchat to LangChain via MCP
Follow these steps to integrate the Freshchat MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 12 tools from Freshchat via MCP
Why Use LangChain with the Freshchat MCP Server
LangChain provides unique advantages when paired with Freshchat through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Freshchat 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 Freshchat queries for multi-turn workflows
Freshchat + LangChain Use Cases
Practical scenarios where LangChain combined with the Freshchat MCP Server delivers measurable value.
RAG with live data: combine Freshchat tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Freshchat, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Freshchat tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Freshchat tool call, measure latency, and optimize your agent's performance
Freshchat MCP Tools for LangChain (12)
These 12 tools become available when you connect Freshchat to LangChain via MCP:
check_account_status
Verify account configuration
get_agent_profile
Get agent metadata
get_chat_user_details
Get user metadata
get_conversation_details
Get chat metadata
list_agent_groups
List agent groups
list_chat_channels
List chat channels
list_chat_messages
List messages in a chat
list_chat_users
List chat participants
list_conversations
List active chats
list_support_agents
List support agents
search_chat_users
Find user by email
send_chat_message
Post a new message
Example Prompts for Freshchat in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Freshchat immediately.
"List all open conversations in my Freshchat account."
"Find the Freshchat user with the email 'customer@example.com'."
"Send a message to conversation 'conv_987': 'I am looking into this for you'."
Troubleshooting Freshchat MCP Server with LangChain
Common issues when connecting Freshchat to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersFreshchat + LangChain FAQ
Common questions about integrating Freshchat 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.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Freshchat with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Freshchat to LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.