LeadSquared MCP Server for LlamaIndex 10 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add LeadSquared as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 LeadSquared. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in LeadSquared?"
)
print(response)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About LeadSquared MCP Server
Connect your LeadSquared account to any AI agent to automate your sales execution and CRM workflows. This MCP server enables your agent to interact with leads, capture new prospects, track activities, and manage sales opportunities directly.
LlamaIndex agents combine LeadSquared 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
- Lead Management — Search, retrieve, and capture leads while maintaining a clean database with automatic deduplication
- Activity Tracking — List and record interactions like calls, meetings, and emails associated with specific leads
- Opportunity Oversight — Create and monitor sales opportunities and their associated activity histories
- Custom Field Support — Access full profile details and custom attributes for all your CRM entities
- Workflow Automation — Identify activity types and update prospect statuses via natural language commands
The LeadSquared 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 LeadSquared to LlamaIndex via MCP
Follow these steps to integrate the LeadSquared 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 LeadSquared
Why Use LlamaIndex with the LeadSquared MCP Server
LlamaIndex provides unique advantages when paired with LeadSquared through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine LeadSquared tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain LeadSquared tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query LeadSquared, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what LeadSquared tools were called, what data was returned, and how it influenced the final answer
LeadSquared + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the LeadSquared MCP Server delivers measurable value.
Hybrid search: combine LeadSquared real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query LeadSquared 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 LeadSquared for fresh data
Analytical workflows: chain LeadSquared queries with LlamaIndex's data connectors to build multi-source analytical reports
LeadSquared MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect LeadSquared to LlamaIndex via MCP:
capture_lead
Capture or update a lead
create_opportunity
Create a new sales opportunity
create_or_update_lead
Uses email or phone as the matching criteria. Create or update a lead profile
create_prospect_activity
g., call log, email sent) to a lead profile. Record a new activity for a lead
get_lead_details
Get details for a specific lead
list_activity_types
g., Phone Call, Meeting) configured in LeadSquared. List all lead activity types
list_lead_activities
List activities for a specific lead
list_opportunities
List sales opportunities
list_opportunity_activities
List activities for a specific opportunity
search_leads
Search for leads with criteria
Example Prompts for LeadSquared in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with LeadSquared immediately.
"Find a lead with email 'test@example.com' in LeadSquared."
"List all sales opportunities in the pipeline."
"Capture a new lead: Name 'Alice Smith', Email 'alice@corp.com'."
Troubleshooting LeadSquared MCP Server with LlamaIndex
Common issues when connecting LeadSquared to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLeadSquared + LlamaIndex FAQ
Common questions about integrating LeadSquared MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect LeadSquared with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect LeadSquared to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.