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Vinkius runs on LlamaIndex

How to Use the Salesquared MCP in LlamaIndex

Index your Salesquared communication logs directly into LlamaIndex.

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Vinkius runs on LlamaIndex

Connect Salesquared MCP to LlamaIndex

Create your Vinkius account to connect Salesquared 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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Key Capabilities

Vectorize Salesquared Campaigns

The `list_campaigns` tool lets LlamaIndex pull your entire outbound marketing history into a searchable vector store. Your RAG setup ingests the campaign parameters, schedules, and targeting rules. You can ask your agent questions about past performance. It runs `get_campaign` to fetch the raw data, embeds the text, and returns answers grounded in actual historical configurations. You stop guessing what worked last quarter.

Query Leads via MCP Server

You use the `list_leads` tool to dump your current prospect roster into a local index. LlamaIndex reads the attributes and makes them semantically searchable. When you need specifics on a single prospect, the agent fires `get_lead`. It combines the live API response with your indexed documents. You get a complete profile of the customer before you ever pick up the phone.

Automate Call Workflows

The `send_voice_otp` tool executes secure authentication calls straight from your LlamaIndex application. You trigger the call when a user tries to access restricted documents in your RAG setup. If they need a custom message, you can string together `create_audio_from_tts` and `create_voice_survey`. The agent handles the audio generation and logs the transcript back into your semantic index for future reference.

Setup guide

Set up Salesquared MCP in LlamaIndex

Prerequisites

  • Python 3.10+ installed
  • llama-index-tools-mcp package
  • Active Vinkius subscription with a valid endpoint token
  1. 1

    Install dependencies

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

  2. 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. 3

    Convert to LlamaIndex tools

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

  4. 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 Salesquared tools.",
)
response = await agent.run("List recent Salesquared data")

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Salesquared. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Salesquared MCP in LlamaIndex

Install llama-index-tools-mcp and instantiate a BasicMCPClient. Pass the Vinkius endpoint URL to the client, and LlamaIndex automatically converts the API into callable tools.
You can create new entries using the `create_lead` tool. The agent formats the extracted entity data from your documents and pushes the payload to the platform.
It indexes the text inputs and metadata you feed into `create_voice_survey`. The actual audio file remains on the communication platform, but the RAG system knows what the survey says.
You combine live communication tools with semantic search. Your RAG app can read a company policy document and immediately text the summary to a team using the text messaging tool.
The `validate_email` payloads route through a zero-trust architecture. We enforce strict isolation so your prospect email addresses never touch disk storage or cross-pollinate with other tenants.

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