How to Use the CallFire MCP in LlamaIndex

Q: How do I index CallFire data into a LlamaIndex vector store?

Use McpToolSpec to load tools like listtexts into your LlamaIndex environment. Run the tool to gather text payloads, then pass those documents directly to your vector index constructor.

Q: Does LlamaIndex query CallFire voice campaigns?

Yes, your LlamaIndex agent executes listcampaigns to retrieve active broadcast details. It then parses this campaign data to answer user queries about outreach progress.

Q: Does LlamaIndex support real-time CallFire webhook tracking?

Query webhook configurations using getwebhook within your LlamaIndex pipeline. This lets your agent verify that data ingestion endpoints are active before starting an index update.

Q: How do I limit which tools the indexer can access?

Pass an allowedtools filter when configuring your MCP tools in LlamaIndex. Restrict the agent to only use listcontacts while hiding campaign management tools.

Index your CallFire text logs and voice campaigns into LlamaIndex vector stores for search.

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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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Index CallFire SMS histories into LlamaIndex

This MCP Server exposes `get_text` and `list_texts` to feed your active SMS logs directly into vector indexes. You build RAG pipelines that ground responses in actual text histories rather than guessing what was sent.

Query campaign data using semantic search

Feed outputs from `get_campaign` and `list_campaigns` through this MCP Server into your LlamaIndex document store to analyze broadcast performance. The agent cross-references past calls from `list_calls` with your indexed documents to identify which scripts worked best.

Map call logs to customer profiles

Match `get_contact` data with call metadata from `get_call` to build a unified search index in LlamaIndex. You check webhook statuses using `list_webhooks` to ensure your indexing pipeline receives real-time updates when calls end.

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

Use `McpToolSpec` to load tools like `list_texts` into your LlamaIndex environment. Run the tool to gather text payloads, then pass those documents directly to your vector index constructor.

Yes, your LlamaIndex agent executes `list_campaigns` to retrieve active broadcast details. It then parses this campaign data to answer user queries about outreach progress.

Query webhook configurations using `get_webhook` within your LlamaIndex pipeline. This lets your agent verify that data ingestion endpoints are active before starting an index update.

Pass an `allowed_tools` filter when configuring your MCP tools in LlamaIndex. Restrict the agent to only use `list_contacts` while hiding campaign management tools.

No, your actual contact details and SMS logs are processed in an ephemeral V8 sandbox. LlamaIndex only stores the vectors you choose to index in your own database; Vinkius never retains your communication data.

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