How to Use the CallFire MCP in LlamaIndex

Q: How do I index CallFire data using LlamaIndex?

You use the McpToolSpec to pull data from your account. Once retrieved, you pass that output into your vector store for indexing.

Index your CallFire data in LlamaIndex. Turn communication logs into a searchable knowledge base.

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Connect CallFire MCP to LlamaIndex

Create your Vinkius account to connect CallFire 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 CallFire with LlamaIndex

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Query CallFire history with LlamaIndex

Feed your past SMS and call logs into a vector store for semantic retrieval. Use `list_sent_sms` to gather data that your agent then indexes. Your agent no longer guesses about past interactions. It searches through actual message content to provide grounded answers about your previous campaigns.

Sync CallFire campaigns to LlamaIndex

Keep your agent informed about current keywords and campaign sounds. By calling `list_keywords`, you bring current account parameters into your searchable index. This prevents your agent from referencing outdated messaging rules. It treats your account settings as live, authoritative documents.

Monitor CallFire webhooks in LlamaIndex

Track your active campaign hooks by indexing the output of `list_webhooks`. This keeps a living record of how your system connects to the outside world. Your RAG pipeline can now answer questions about your current integrations. It makes your backend configuration a searchable part of your knowledge base.

Setup guide

Set up CallFire 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 CallFire 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 CallFire tools.",
)
response = await agent.run("List recent CallFire data")

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Common questions about CallFire MCP in LlamaIndex

Yes, the server pulls your message history and converts it into indexable text. Your agent then queries this data to identify patterns in your communications.

You use the `McpToolSpec` to pull data from your account. Once retrieved, you pass that output into your vector store for indexing.

Only the data you choose to index stays in your vector store. The server itself is just a temporary conduit for fetching your account details.

Your API keys and account details are isolated within the MCP sandbox. No external parties can view the communication records you index for your agents.

We enforce strict transport security for every request. The data is transferred directly to your local LlamaIndex environment without being cached on our systems.

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