How to Use the Freshcaller MCP in LlamaIndex

Q: How do I filter Freshcaller tools in LlamaIndex?

Use the allowedtools parameter when initializing your tool spec. This lets you restrict access to sensitive operations like export jobs.

Index your Freshcaller history in LlamaIndex to query call records and agent data with semantic search.

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Connect Freshcaller MCP to LlamaIndex

Create your Vinkius account to connect Freshcaller to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Freshcaller with LlamaIndex

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Index Freshcaller calls for RAG

Take the output of `list_calls` and feed it into your vector store. Your LlamaIndex application gains immediate access to historical call data for grounded, accurate answers. Stop relying on static logs. Now you can query your call history using natural language and get results based on actual recorded data.

Query agent details with LlamaIndex

Use `get_agent_details` to build a knowledge base of your support staff. Your index will understand who handles which calls and how teams are structured. This setup allows your agent to answer complex questions about team performance. It pulls data directly from the API and indexes it for fast, semantic retrieval.

Retrieve call recordings for analysis

Fetch links via `get_call_recording` and store them within your LlamaIndex knowledge base. You can then reference these files when your agent summarizes support interactions. Everything is indexed and queryable. When you need to find a specific call, your agent knows exactly where the recording lives and provides the link instantly.

Setup guide

Set up Freshcaller MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Freshcaller MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Freshcaller tools.",
)
response = await agent.run("List recent Freshcaller data")

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Common questions about Freshcaller MCP in LlamaIndex

You use the MCP tool spec to fetch data from the server and pass it into your index pipeline. The data becomes searchable via your existing vector store logic.

It depends on your refresh frequency. Re-run your indexing tools periodically to ensure your agent is working with the latest call and agent records.

Your data is only processed through the MCP server connection. We don't store your records, keeping your sensitive support logs confined to your local index.

Yes, you can mix API output with local documents. The index treats them all as a unified knowledge base, allowing for cross-referenced insights.

Use the allowed_tools parameter when initializing your tool spec. This lets you restrict access to sensitive operations like export jobs.

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