HighLevel MCP Server: Connect AI Agents to HighLevel Tools

Modified on: Sun, 28 Jun, 2026 at 1:07 AM

The HighLevel MCP Server lets AI agents and MCP-compatible clients connect securely to HighLevel tools and services. With MCP, AI assistants can retrieve records, update data, send messages, search opportunities, access calendar information, and automate tasks through a standardized HTTP connection.

TABLE OF CONTENTS

What is HighLevel MCP Server?
Key Benefits of HighLevel MCP Server
Supported MCP Clients
Prerequisites
How To Connect to the HighLevel MCP Server
Recommended Scopes
Available MCP Tools
Example MCP Workflows
Using MCP in AI Clients
Security and Authentication
Best Practices
Frequently Asked Questions
Related Articles

What is HighLevel MCP Server?

The HighLevel MCP Server uses the Model Context Protocol, or MCP, to let AI agents securely interact with HighLevel services. MCP acts as a bridge between an AI client and HighLevel, allowing the AI client to discover available tools, query data, and perform approved actions.

The MCP Server can be used with supported AI clients and developer tools to interact with HighLevel services such as Contacts, Conversations, Calendars, Opportunities, Payments, Locations, and Custom Fields.

Production MCP Endpoint

https://services.leadconnectorhq.com/mcp/

Key Benefits of HighLevel MCP Server

The MCP Server helps AI builders connect intelligent assistants to HighLevel data and actions without building a custom integration from scratch.

Centralized AI Access: Connect AI agents to multiple HighLevel services through one MCP endpoint.
Secure Authentication: Use Private Integration Tokens and scoped permissions to control access.
Natural Language Automation: Let AI assistants perform supported actions from plain-language prompts.
Multi-Service Connectivity: Work with contacts, conversations, calendars, opportunities, payments, locations, and more.
MCP-Compatible Flexibility: Connect supported clients such as Cursor, Windsurf, OpenAI Playground, Claude-compatible clients, and custom MCP applications.
No SDK Required: Use a standardized HTTP-based MCP connection instead of building a full custom integration.

Supported MCP Clients

MCP-compatible clients allow AI agents to discover and run HighLevel tools through a connected server. Support depends on whether the client can connect to HTTP-based MCP servers and pass the required authentication headers.

Supported client examples include:

Cursor
Windsurf
OpenAI Playground
Claude-compatible MCP clients
Custom MCP-compatible applications
Other HTTP-based MCP clients

Prerequisites

A correct setup helps ensure the AI client can access only the HighLevel data and actions it needs.

Before connecting to the HighLevel MCP Server, make sure you have:

Access to the desired HighLevel sub-account/location
A Private Integration Token
Required scopes enabled for the token
The sub-account/location ID
An MCP-compatible client

How To Connect to the HighLevel MCP Server

Connecting an MCP client requires a Private Integration Token, the HighLevel MCP endpoint, and the location ID. Once connected, the AI client can discover available tools based on the scopes granted to the token.

Step 1: Create a Private Integration Token

Private Integration Tokens allow your MCP client to authenticate securely with HighLevel.

Log in to HighLevel.
Open the desired sub-account/location.
Go to Settings.
Select Private Integrations.
Click Create New Integration.
Choose the required scopes.
Click Create Integration.
Copy and securely store the generated token.

Step 2: Add the MCP Server to Your Client

Add the MCP endpoint and authentication headers to your MCP-compatible client.

{  "mcpServers": {    "prod-ghl-mcp": {      "url": "https://services.leadconnectorhq.com/mcp/",      "headers": {        "Authorization": "Bearer <your-token>",        "locationId": "<your-location-id>"      }    }  } }

Replace:

<your-token> with your Private Integration Token
<your-location-id> with the HighLevel sub-account/location ID

Step 3: Select the Required Tools

After the client connects, available tools appear based on the scopes assigned to the Private Integration Token. Select only the tools your AI agent needs for the workflow.

Recommended Scopes

Scopes control which HighLevel resources the MCP client can read or modify. Grant only the permissions required for your workflow.

Recommended scopes may include:

Contacts: View Contacts, Edit Contacts
Conversations: View Conversations, Edit Conversations
Conversation Messages: View Conversation Messages, Edit Conversation Messages
Opportunities: View Opportunities, Edit Opportunities
Calendars: View Calendars, Edit Calendars
Calendar Events: View Calendar Events, Edit Calendar Events
Payments: View Payment Orders, View Payment Transactions
Other: View Custom Fields, View Forms, View Locations

Available MCP Tools

The HighLevel MCP Server includes tools across core HighLevel services. The tools available to your client depend on the permissions granted through the Private Integration Token.

Calendar Tools

Calendar tools help AI agents retrieve scheduling information and appointment details.

calendars_get-calendar-events
calendars_get-appointment-notes

Contact Tools

Contact tools allow AI agents to find, create, update, and manage contact records.

contacts_get-all-tasks
contacts_add-tags
contacts_remove-tags
contacts_get-contact
contacts_update-contact
contacts_upsert-contact
contacts_create-contact
contacts_get-contacts

Conversation Tools

Conversation tools allow AI agents to search conversations, review message history, and send supported messages.

conversations_search-conversation
conversations_get-messages
conversations_send-a-new-message

Location Tools

Location tools allow AI agents to retrieve location details and custom field information.

locations_get-location
locations_get-custom-fields

Opportunity Tools

Opportunity tools allow AI agents to search, retrieve, and update pipeline opportunities.

opportunities_search-opportunity
opportunities_get-pipelines
opportunities_get-opportunity
opportunities_update-opportunity

Payment Tools

Payment tools allow AI agents to retrieve order and transaction data.

payments_get-order-by-id
payments_list-transactions

Example MCP Workflows

MCP allows AI agents to interact with HighLevel using natural language while executing approved tools in the background.

Common workflows include:

Searching for a contact by name, email, or phone number
Creating or updating a contact
Adding or removing contact tags
Searching conversation history
Sending a message to a contact
Retrieving calendar events
Searching opportunities
Updating opportunity details
Reviewing payment order or transaction data

Using MCP in AI Clients

AI clients can use the HighLevel MCP Server to discover tools and complete actions inside HighLevel. After the MCP server is connected, the client can call available tools based on the user’s prompt and the token’s scopes.

For example, a user could ask:

Check if I have a contact named Bruce Wayne.

The AI client can use contact tools to search HighLevel and return matching records.

Another example:

Search for available opportunities in my account.

The AI client can call opportunity tools and summarize the results.

Security and Authentication

The MCP Server uses token-based authentication to protect access to HighLevel data. Each request must include a valid Private Integration Token and location ID.

Required authentication details:

Private Integration Token
locationId
Required scopes for the requested tools

Keep tokens private and never expose them in public repositories, screenshots, shared prompts, or client-side code.

Best Practices

Following MCP best practices helps keep AI workflows secure, reliable, and easier to manage.

Use least-privilege access: Enable only the scopes required for the AI workflow.
Store tokens securely: Keep Private Integration Tokens in secure storage and avoid sharing them publicly.
Review write permissions carefully: Grant edit permissions only when the AI agent needs to create or update data.
Test before production use: Confirm the AI client calls the correct tools and returns expected results.
Monitor AI activity: Review AI-driven actions when enabling write access.
Rotate tokens periodically: Replace tokens regularly or when access should be revoked.
Use clear prompts: Give the AI agent specific instructions about when it should read data, update records, or ask for confirmation.

Frequently Asked Questions

Q: What does MCP stand for?
MCP stands for Model Context Protocol. It is a standardized protocol that allows AI agents and copilots to connect with external tools and services.

Q: What does the HighLevel MCP Server do?
It allows MCP-compatible AI clients to securely access supported HighLevel tools, retrieve data, and perform approved actions.

Q: Which HighLevel services are supported?
The MCP Server supports tools for contacts, conversations, calendars, opportunities, payments, locations, and custom fields.

Q: How do I authenticate with the MCP Server?
Authentication requires a Private Integration Token and a valid location ID added to the MCP client configuration headers.

Q: Can AI agents update HighLevel data through MCP?
Yes. AI agents can update data when the connected token includes the required edit scopes.

Q: Can I limit what an AI client can access?
Yes. Access is controlled through the scopes assigned to the Private Integration Token.

Q: Can I use MCP with Cursor or Windsurf?
Yes. MCP-compatible clients such as Cursor and Windsurf can connect when they support the required HTTP MCP configuration.

Q: Can I use MCP with OpenAI Playground?
Yes. OpenAI Playground can connect to remote MCP servers when configured with the endpoint and required headers.

Q: Is OAuth supported?
OAuth support is planned for a future release. Use Private Integration Tokens for the current setup.

Q: Can I connect custom AI agents to the HighLevel MCP Server?
Yes. Custom applications can connect if they support MCP over HTTP and can send the required authentication headers.