Boostapp MCP Server for LlamaIndexGive LlamaIndex instant access to 1 tools to Create Lead

Name: Boostapp
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

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The Boostapp MCP Server for LlamaIndex is a standout in the Sales Automation category — giving your AI agent 1 tools to work with, ready to go from day one.

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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 Boostapp. "
            "You have 1 tools available."
        ),
    )

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

asyncio.run(main())

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About Boostapp MCP Server

Connect your Boostapp account to any AI agent to streamline your sales and customer acquisition workflows. This integration allows your AI to act as a virtual sales assistant, capturing lead information and organizing it within your CRM through natural conversation.

LlamaIndex agents combine Boostapp tool responses with indexed documents for comprehensive, grounded answers. Connect 1 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

Lead Creation — Instantly create new leads with full names and contact numbers directly in the Boostapp system.
Pipeline Management — Automatically assign leads to specific pipeline stages and identify lead sources using system IDs.
Detailed Profiling — Capture comprehensive lead data including email addresses, birth dates, gender, and custom remarks.
Subscription Integration — Link new leads to specific items or subscriptions during the creation process.

The Boostapp MCP Server exposes 1 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 1 Boostapp tools available for LlamaIndex

When LlamaIndex connects to Boostapp through Vinkius, your AI agent gets direct access to every tool listed below — spanning crm, lead-management, sales-pipeline, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Create lead on Boostapp

Requires full name and phone number. Can optionally include pipeline stage and subscription details. Create a new lead in Boostapp

Connect Boostapp to LlamaIndex via MCP

Follow these steps to wire Boostapp into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 1 tools from Boostapp

Why Use LlamaIndex with the Boostapp MCP Server

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

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

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

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

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

Boostapp + LlamaIndex Use Cases

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

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

Data enrichment: query Boostapp 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 Boostapp for fresh data

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

Example Prompts for Boostapp in LlamaIndex

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

"Create a new lead for John Smith with phone 0501234567 and email john@example.com."

"Add a lead named Sarah Connor, phone 0549876543, and set her pipeline stage to 2."

"Register a lead for Mike Ross (0521112233) with the remark 'Interested in the annual subscription' and item ID 101."

Troubleshooting Boostapp MCP Server with LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

Boostapp + LlamaIndex FAQ

Common questions about integrating Boostapp 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 Boostapp tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.