Can I check the source of a specific call using the agent?

Yes! Use the `get_call_details` tool with the Call ID. Your agent will fetch the detailed metadata, which typically includes the `source` or `tracking_number` associated with the call.

How do I list all the leads from my website forms?

Simply ask the agent to `list_form_submissions`. It will retrieve the latest leads captured through web forms from your CallRail account, including names and submission times.

Does the integration allow creating a new tracking number?

The current toolset focuses on querying and monitoring (Read-Only) for marketing attribution analysis. Provisioning new numbers or changing core routing should be managed via the CallRail administrator dashboard.

CallRail MCP Server for LlamaIndex 10 tools — connect in under 2 minutes

Name: CallRail
Availability: InStock
Author: Vinkius

docs.llamaindex.ai/en/stable/module_guides/tools/mcp

Built by Vinkius GDPR 10 Tools Framework

LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CallRail as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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 CallRail. "
            "You have 10 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in CallRail?"
    )
    print(response)

asyncio.run(main())

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About CallRail MCP Server

Connect your CallRail account to any AI agent and orchestrate your call tracking, lead management, and marketing attribution workflows through natural conversation.

LlamaIndex agents combine CallRail tool responses with indexed documents for comprehensive, grounded answers. Connect 10 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.

What you can do

Call Oversight — List all tracked phone calls and retrieve detailed metadata, including durations, tracking numbers, and statuses.
Lead Management — Access leads generated via web forms and monitor their conversion journey directly from your workspace.
Company Coordination — List and retrieve detailed profiles for all companies and clients managed within the account.
Tracker Oversight — Monitor all active tracking numbers and their respective sources to ensure data accuracy.
User & Team Management — Access your directory of users and agents to maintain visibility across your organization.
Alert Monitoring — Retrieve and monitor active account alerts to stay on top of critical issues.

The CallRail MCP Server exposes 10 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 CallRail to LlamaIndex via MCP

Follow these steps to integrate the CallRail 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 10 tools from CallRail

Why Use LlamaIndex with the CallRail MCP Server

LlamaIndex provides unique advantages when paired with CallRail through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine CallRail tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain CallRail tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query CallRail, a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what CallRail tools were called, what data was returned, and how it influenced the final answer

CallRail + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the CallRail MCP Server delivers measurable value.

Hybrid search: combine CallRail real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query CallRail 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 CallRail for fresh data

Analytical workflows: chain CallRail queries with LlamaIndex's data connectors to build multi-source analytical reports

CallRail MCP Tools for LlamaIndex (10)

These 10 tools become available when you connect CallRail to LlamaIndex via MCP:

get_account_info

Retrieve core account information

get_call_details

Get details of a specific phone call

get_company_details

Get details of a specific company

list_alerts

List active account alerts

list_calls

List all tracked phone calls

list_companies

List all companies associated with the account

list_form_submissions

List leads generated via web forms

list_tags

List all lead and call tags

list_trackers

List all tracking numbers and sources

list_users

List all users in the account

Example Prompts for CallRail in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with CallRail immediately.

"List all my calls from today in CallRail."

"Show the details for form submission with ID 99283."

"List all the companies in my CallRail account."

Troubleshooting CallRail MCP Server with LlamaIndex

Common issues when connecting CallRail to LlamaIndex through the Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

CallRail + LlamaIndex FAQ

Common questions about integrating CallRail MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query CallRail tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.