How to Use the CRM PipeRun MCP in LlamaIndex

Q: Can I filter which CRM PipeRun activities LlamaIndex ingests?

Yes. The listactivities tool has filtering capabilities. You can configure your data ingestion pipeline to only pull activities of a certain type or status, so your index only contains the data you care about.

Q: What's the setup for connecting CRM PipeRun to LlamaIndex?

Install llama-index-tools-mcp and instantiate the McpToolSpec with your Vinkius URL. Then you can get the tools and pass them to your agent. It’s a few lines of code to connect your agent to the CRM.

Q: Can my agent find files attached to a deal in PipeRun?

Yes. The listfiles tool can be used to index metadata about files attached to deals, people, or companies. Your agent can then search this index to find relevant documents based on your query.

Turn your live CRM PipeRun data into a searchable knowledge base with LlamaIndex.

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Connect CRM PipeRun MCP to LlamaIndex

Create your Vinkius account to connect CRM PipeRun to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup CRM PipeRun with LlamaIndex

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Index Your Sales Activities

This server exposes your PipeRun data through tools like `list_activities` and `list_files`. With LlamaIndex, you don't just call these tools—you index their output. Your agent can pull down all recent sales tasks and build a vector index from them. Now, your application has a memory of your sales pipeline. When you ask a question, the agent queries this local index first. It's faster than hitting the API every time and lets you perform semantic searches over your CRM history.

Get Answers from Your CRM Data

Stop getting vague answers from your AI. By building a knowledge base from PipeRun, your agent's responses are grounded in actual data. Ask "What was the last thing I did for the Acme deal?" and LlamaIndex will find the relevant indexed activity. If the indexed data is stale, the agent can use the `get_activity` tool to fetch the very latest details directly from the API. This RAG pattern—retrieving from an index, then augmenting with live data—gives you accurate, up-to-date answers.

Build RAG Apps on Your MCP Server

This MCP Server is your bridge to live PipeRun data. Use it with LlamaIndex to build powerful RAG applications. You can create a unified index that combines your CRM activities with other sources, like product documentation or sales playbooks. Your agent can now answer complex questions by drawing from multiple sources at once. It can find a relevant task with `list_activities`, then cross-reference it with a sales script from a PDF, all in one query.

Setup guide

Set up CRM PipeRun 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 CRM PipeRun 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 CRM PipeRun tools.",
)
response = await agent.run("List recent CRM PipeRun data")

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

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Common questions about CRM PipeRun MCP in LlamaIndex

LlamaIndex uses the MCP tools to fetch data, like activities and files, from CRM PipeRun. It then ingests this data into a searchable index, creating a knowledge base that your agent can query to answer questions.

Yes. The `list_activities` tool has filtering capabilities. You can configure your data ingestion pipeline to only pull activities of a certain type or status, so your index only contains the data you care about.

Install `llama-index-tools-mcp` and instantiate the `McpToolSpec` with your Vinkius URL. Then you can get the tools and pass them to your agent. It’s a few lines of code to connect your agent to the CRM.

Yes. The `list_files` tool can be used to index metadata about files attached to deals, people, or companies. Your agent can then search this index to find relevant documents based on your query.

The MCP Server itself doesn't store anything. LlamaIndex will process your activity and file data to create its index, which is stored and managed by your application. Vinkius provides a zero-trust, ephemeral execution for each tool call.

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