How to Use the HighLevel MCP in LlamaIndex

Q: How do I index HighLevel contacts with LlamaIndex?

Use the HighLevel MCP Server tool spec to pull data via searchcontacts. LlamaIndex then parses the resulting JSON string into Document objects for your vector store.

Q: Can LlamaIndex agents write back to HighLevel calendars?

Yes. Your LlamaIndex agent uses createappointment to book slots. It checks availability first using indexed data from getcalendarfreeslots.

Q: Can I query HighLevel tasks using LlamaIndex?

Yes. You can fetch task lists using listcontacttasks and index them. This lets your agent perform semantic searches over pending items to prioritize daily work.

Index live HighLevel CRM data into your LlamaIndex vector stores for grounded RAG and booking workflows.

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Connect HighLevel MCP to LlamaIndex

Create your Vinkius account to connect HighLevel 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 HighLevel with LlamaIndex

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Index HighLevel contact details for LlamaIndex RAG

`get_contact_details` serves as the primary data fetcher to feed live CRM records into your LlamaIndex document store via this MCP Server. The framework indexes these details so your agent can query past client interactions semantically. By combining this with `search_contacts`, your LlamaIndex query engine retrieves contextually relevant lead profiles during active chat sessions. This prevents your agent from hallucinating customer history.

Ground calendar scheduling in LlamaIndex

`get_calendar_free_slots` retrieves raw calendar availability to be indexed and queried by your LlamaIndex engine. Your agent searches these slots like a local document database to find optimal meeting times. When the agent finds a match, it calls `create_appointment` to finalize the booking. This keeps your scheduling workflow grounded in real-time calendar data instead of outdated static files.

Query HighLevel pipeline stages using LlamaIndex

`list_pipelines` feeds your current sales structure directly into LlamaIndex's index structure. Your agent queries this index to understand how your sales stages are organized. The agent then uses `search_opportunities` to find where specific deals sit within those pipelines. This lets you build RAG applications that answer complex questions about your active sales funnel.

Setup guide

Set up HighLevel 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 HighLevel 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 HighLevel tools.",
)
response = await agent.run("List recent HighLevel 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 HighLevel. 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 HighLevel MCP in LlamaIndex

Use the HighLevel MCP Server tool spec to pull data via `search_contacts`. LlamaIndex then parses the resulting JSON string into Document objects for your vector store.

Yes. Your LlamaIndex agent uses `create_appointment` to book slots. It checks availability first using indexed data from `get_calendar_free_slots`.

Yes, the server exposes all 11 tools directly as LlamaIndex-compatible function tools. You load them using the basic MCP client wrapper.

Yes. You can fetch task lists using `list_contact_tasks` and index them. This lets your agent perform semantic searches over pending items to prioritize daily work.

Your data stays completely isolated. Vinkius processes all contact and calendar payloads through this MCP Server inside ephemeral sandboxes, meaning your raw CRM records are never stored on our servers.

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