How to Use the Pipeliner MCP in LlamaIndex

Q: How do I start using the Pipeliner tools with my LlamaIndex app?

You'll use the McpToolSpec to wrap the client. This exposes all the Pipeliner tools so your agent can call them directly or use them as part of a data ingestion pipeline for your index.

Turn your Pipeliner CRM into a searchable knowledge base for your LlamaIndex agent. Ask questions, get answers from your own data.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers — Included with Plan

Connect Pipeliner MCP to LlamaIndex

Create your Vinkius account to connect Pipeliner to LlamaIndex — we handle the hosting, security, and runtime updates so you don't have to. No server setup required.

GDPR Included with Plan

Setup Pipeliner with LlamaIndex

Ask AI about this MCP

ChatGPT

Claude

Perplexity Vinkius runs on Gemini

Gemini

Grok

Index Your Live CRM Data

LlamaIndex doesn't just call a tool once. It can systematically call Pipeliner tools and index the results into a vector store. This creates a searchable knowledge base from your live sales data. Your agent can run `list_pipeliner_opportunities` and `list_pipeliner_leads` daily, indexing every detail. When you ask "which deals stalled last week?", the agent queries the index for a fast, accurate answer instead of making multiple live API calls.

Ground Your LlamaIndex Agent in Facts

Stop agent hallucinations. By building a Retrieval-Augmented Generation (RAG) app with LlamaIndex, your agent's answers are based on the actual data it indexed from your Pipeliner account. It's not guessing; it's citing facts. Ask your agent for a summary of a contact's history. It will query its index for every interaction logged in `list_pipeliner_activities` and use that specific data to build its response. The answers are tied directly to your CRM records.

Query Your Pipeliner History

This MCP Server lets you populate a knowledge base with your CRM's state. You're not just querying what's happening now; you're building a history of your pipeline you can analyze over time. For instance, you can index the output of `list_pipeliner_tasks` every day. A month later, you can ask your LlamaIndex agent "what was the average task load for the sales team in May?" and get an answer based on the indexed snapshots.

Setup guide

Set up Pipeliner MCP in LlamaIndex

Prerequisites

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

1

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

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Pipeliner MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
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 Pipeliner tools.",
)
response = await agent.run("List recent Pipeliner data")

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Pipeliner. 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.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect Pipeliner now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about Pipeliner MCP in LlamaIndex

Yes, if you've configured it to index the data. You can set up a process to run `list_pipeliner_opportunities` periodically and add the results to your LlamaIndex vector store. The agent can then query that historical data.

LlamaIndex is built for RAG. It excels at turning the output of tools like `list_pipeliner_contacts` into a structured, searchable index. This gives your agent a memory of your CRM data.

You'll use the `McpToolSpec` to wrap the client. This exposes all the Pipeliner tools so your agent can call them directly or use them as part of a data ingestion pipeline for your index.

Yes, that's the point. You can index your internal sales playbooks and combine that with live data pulled from Pipeliner using tools like `list_pipeliner_leads`. Your agent can then answer questions using both sources.

The Vinkius MCP server itself is stateless; it just fetches the data for you. When you use LlamaIndex to create an index, that indexed data (contacts, leads, etc.) lives in *your* vector store. The server only handles the real-time access and never sees your stored index.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript