Pipeliner 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 Pipeliner 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 Pipeliner. "
"You have 10 tools available."
),
)
response = await agent.run(
"What tools are available in Pipeliner?"
)
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 Pipeliner MCP Server
Connect your Pipeliner CRM space to any AI agent and take full control of your sales ecosystem through natural conversation.
LlamaIndex agents combine Pipeliner 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 & Opportunity Oversight — List and retrieve detailed metadata for leads and sales opportunities across your workspace.
- Sales Pipeline Management — List available pipelines and track the progress of deals through different stages.
- Workforce Visibility — List company accounts, business contacts, and team members to maintain a clear view of your stakeholders.
- Activity & Task Tracking — Monitor sales activities and assigned tasks to ensure your team stays productive.
- Detailed Entity Inspections — Get deep-dive details for any specific lead or opportunity to understand its full history.
The Pipeliner 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 Pipeliner to LlamaIndex via MCP
Follow these steps to integrate the Pipeliner 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 Pipeliner
Why Use LlamaIndex with the Pipeliner MCP Server
LlamaIndex provides unique advantages when paired with Pipeliner through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Pipeliner tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Pipeliner tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Pipeliner, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Pipeliner tools were called, what data was returned, and how it influenced the final answer
Pipeliner + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Pipeliner MCP Server delivers measurable value.
Hybrid search: combine Pipeliner real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Pipeliner 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 Pipeliner for fresh data
Analytical workflows: chain Pipeliner queries with LlamaIndex's data connectors to build multi-source analytical reports
Pipeliner MCP Tools for LlamaIndex (10)
These 10 tools become available when you connect Pipeliner to LlamaIndex via MCP:
get_pipeliner_lead
Get details for a specific lead
get_pipeliner_opportunity
Get details for a specific opportunity
list_pipeliner_accounts
List all company accounts
list_pipeliner_activities
List sales activities and tasks
list_pipeliner_contacts
List all business contacts
list_pipeliner_leads
List all sales leads
list_pipeliner_opportunities
List all sales opportunities
list_pipeliner_pipelines
List available sales pipelines
list_pipeliner_tasks
List all assigned tasks
list_pipeliner_users
List users in the Pipeliner space
Example Prompts for Pipeliner in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Pipeliner immediately.
"List all sales opportunities in the 'Enterprise' pipeline."
"Show me the last 5 leads added to Pipeliner."
"What are my sales activities for this week?"
Troubleshooting Pipeliner MCP Server with LlamaIndex
Common issues when connecting Pipeliner to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpPipeliner + LlamaIndex FAQ
Common questions about integrating Pipeliner 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 Pipeliner 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 Pipeliner to LlamaIndex
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.