Freshcaller MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Freshcaller through 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({
"freshcaller": {
"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 Freshcaller, 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 Freshcaller MCP Server
Connect your Freshcaller (now Freshdesk Contact Center) account to any AI agent to automate your cloud telephony and contact center management through the Model Context Protocol (MCP). Freshcaller is a modern phone system that enables teams to handle customer calls across the globe with zero hardware. This MCP server enables you to track call logs, monitor agent performance, and retrieve recording links directly through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Freshcaller 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.
Key Features
- Call Oversight — List all inbound and outbound calls, fetch detailed metadata including duration and status, and monitor recent activity instantly.
- Agent Management — Access your database of users and agents to maintain full context of who is online and handling calls.
- Team Coordination — List configured agent teams and retrieve metadata for specific groups to optimize your routing.
- Recording Retrieval — Get direct links to call recordings for quality assurance and training purposes directly from your chat interface.
- Performance Metrics — Access real-time account metrics to understand call volumes and service levels across your organization.
- Number Inventory — List owned phone numbers and search for new available numbers to scale your global presence.
- Data Export — Monitor initiated export jobs to ensure your historical data is ready for deep analysis.
The Freshcaller 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 Freshcaller to LangChain via MCP
Follow these steps to integrate the Freshcaller 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 Freshcaller via MCP
Why Use LangChain with the Freshcaller MCP Server
LangChain provides unique advantages when paired with Freshcaller through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents. combine Freshcaller 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 Freshcaller queries for multi-turn workflows
Freshcaller + LangChain Use Cases
Practical scenarios where LangChain combined with the Freshcaller MCP Server delivers measurable value.
RAG with live data: combine Freshcaller tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Freshcaller, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Freshcaller tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Freshcaller tool call, measure latency, and optimize your agent's performance
Freshcaller MCP Tools for LangChain (12)
These 12 tools become available when you connect Freshcaller to LangChain via MCP:
get_agent_details
Get agent metadata
get_call_details
Get call metadata
get_call_recording
Get recording link
get_export_status
Check export job
get_team_details
Get team metadata
list_account_metrics
Get call center metrics
list_agent_teams
List agent teams
list_agents
List call center agents
list_buyable_numbers
Search for phone numbers
list_calls
List recent phone calls
list_export_jobs
List data exports
list_my_numbers
List owned phone numbers
Example Prompts for Freshcaller in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Freshcaller immediately.
"List my 5 most recent calls and their duration."
"Show me the status of all agents in my support team."
"Get the recording link for call 'call_abc123'."
Troubleshooting Freshcaller MCP Server with LangChain
Common issues when connecting Freshcaller to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersFreshcaller + LangChain FAQ
Common questions about integrating Freshcaller 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 Freshcaller 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 Freshcaller to LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.
