Freshcaller MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Freshcaller as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Freshcaller. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Freshcaller?"
)
print(response)
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.
LlamaIndex agents combine Freshcaller tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Freshcaller MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 12 tools from Freshcaller
Why Use LlamaIndex with the Freshcaller MCP Server
LlamaIndex provides unique advantages when paired with Freshcaller through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Freshcaller tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Freshcaller tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Freshcaller, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Freshcaller tools were called, what data was returned, and how it influenced the final answer
Freshcaller + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Freshcaller MCP Server delivers measurable value.
Hybrid search: combine Freshcaller real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Freshcaller to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Freshcaller for fresh data
Analytical workflows: chain Freshcaller queries with LlamaIndex's data connectors to build multi-source analytical reports
Freshcaller MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Freshcaller to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Freshcaller to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpFreshcaller + LlamaIndex FAQ
Common questions about integrating Freshcaller MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.