Leadfeeder MCP Server for LlamaIndex 9 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Leadfeeder 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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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 Leadfeeder. "
"You have 9 tools available."
),
)
response = await agent.run(
"What tools are available in Leadfeeder?"
)
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 Leadfeeder MCP Server
Connect your Leadfeeder tracking system to an AI agent to analyze high-quality B2B internet traffic. Track precise analytics without using heavy third-party dashboard setups directly in Cursor or Claude.
LlamaIndex agents combine Leadfeeder tool responses with indexed documents for comprehensive, grounded answers. Connect 9 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
- Discover Target Leads: Fetch the list of verified companies engaging with your tracking pixel on specific domains.
- Visitor Analytics: Drill into session specifics of organizations interacting behind the scenes.
- Sales Pipeline: Identify key B2B traffic and prioritize new cold email targets or warm follow-ups immediately.
The Leadfeeder MCP Server exposes 9 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 Leadfeeder to LlamaIndex via MCP
Follow these steps to integrate the Leadfeeder 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 9 tools from Leadfeeder
Why Use LlamaIndex with the Leadfeeder MCP Server
LlamaIndex provides unique advantages when paired with Leadfeeder through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Leadfeeder tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Leadfeeder tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Leadfeeder, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Leadfeeder tools were called, what data was returned, and how it influenced the final answer
Leadfeeder + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Leadfeeder MCP Server delivers measurable value.
Hybrid search: combine Leadfeeder real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Leadfeeder 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 Leadfeeder for fresh data
Analytical workflows: chain Leadfeeder queries with LlamaIndex's data connectors to build multi-source analytical reports
Leadfeeder MCP Tools for LlamaIndex (9)
These 9 tools become available when you connect Leadfeeder to LlamaIndex via MCP:
get_account
Get details for a specific Leadfeeder account
get_custom_feed
Get details for a specific custom feed filter
get_lead
Get details for a specific lead
get_tracking_script
Get the tracking script for the account
list_account_visits
Get aggregate visits data across the entire account
list_accounts
Retrieve a list of accounts from Leadfeeder
list_custom_feeds
Retrieve the custom feeds active within a specific account
list_lead_visits
Get the website visits directly associated with a specific lead
list_leads
Retrieve a list of discovered leads within an account
Example Prompts for Leadfeeder in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Leadfeeder immediately.
"Analyze and list all identified corporate visitors targeting my site."
"Are there any manufacturing sector companies viewing our price points?"
"Highlight repeat prospects viewing documentation sections."
Troubleshooting Leadfeeder MCP Server with LlamaIndex
Common issues when connecting Leadfeeder to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLeadfeeder + LlamaIndex FAQ
Common questions about integrating Leadfeeder 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 Leadfeeder 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 Leadfeeder to LlamaIndex
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.