Connect Highlevel to ChatGPT: Automate leads and sales pipelines

You want to connect Highlevel to ChatGPT so your AI agents can read contacts, update sales pipelines, and draft outreach messages based on real-time CRM context. If your team uses Claude instead, check out our guide on connecting Highlevel to Claude or explore our broader architectural overview on connecting Highlevel to AI Agents.

Marketing agencies and sales teams run their entire operations on Highlevel. Giving a Large Language Model (LLM) read and write access to that ecosystem is a massive operational advantage, but an equally massive engineering challenge. You either spend weeks building, hosting, and maintaining a custom Model Context Protocol (MCP) server, or you use a managed infrastructure layer that handles the boilerplate for you.

This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for Highlevel, connect it natively to ChatGPT, and execute complex CRM workflows using natural language.

The Engineering Reality of the Highlevel API

A custom MCP server is a self-hosted integration layer that translates an LLM's tool calls into REST API requests. While the open MCP standard provides a predictable way for models to discover tools, the reality of implementing it against Highlevel's APIs is painful.

If you decide to build a custom MCP server for Highlevel, you own the entire API lifecycle. Here are the specific integration challenges that break standard CRUD assumptions when working with this platform:

The Agency and Location Hierarchy

Highlevel is designed for agencies. Its data model is strictly segregated into the Agency level and the Location (Sub-account) level. Almost every operational endpoint - contacts, opportunities, conversations, calendars - requires a locationId (or altId and altType set to location). If your MCP server does not inject or enforce this context, the LLM will fail to fetch data. You have to explicitly instruct the LLM on how to resolve and pass these location constraints on every tool call.

Pipeline and Stage Resolution

When an LLM wants to move a lead to a new pipeline stage, it cannot just send a string like "Demo Scheduled". Highlevel requires strict GUIDs for pipelineId and pipelineStageId. Your agent must first query the pipelines endpoint, parse the nested JSON to find the correct stage ID, and then construct the update payload. If your MCP server's schemas are incomplete, the LLM will hallucinate these IDs and corrupt your CRM state.

Rate Limits and 429 Errors

Highlevel enforces aggressive rate limits, especially on search and bulk endpoints. If your AI agent gets stuck in a loop or tries to summarize too many opportunities at once, Highlevel will reject the requests.

A critical factual note on rate limits: Truto does not retry, throttle, or apply backoff on rate limit errors. When an upstream API like Highlevel returns an HTTP 429, Truto passes that error directly to the caller. Truto normalizes the upstream rate limit info into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF specification. The caller (your AI agent framework or MCP client) is entirely responsible for implementing retry and exponential backoff logic. Truto does not automatically absorb these errors.

The Managed MCP Approach

Instead of forcing your engineering team to build a custom server, manage OAuth lifecycles, and hand-code JSON schemas for every Highlevel endpoint, Truto generates MCP tools dynamically.

Truto derives these tools from an integration's existing resource definitions and documentation records. When a user connects their Highlevel account, Truto's proxy API engine evaluates the API methods, generates descriptive snake_case tool names, translates YAML documentation into rigid JSON Schemas, and serves them over a standardized JSON-RPC 2.0 endpoint.

The resulting MCP server is scoped to a single integrated account, authenticated by a cryptographic token in the URL, and ready to be plugged directly into ChatGPT.

How to Create the Highlevel MCP Server

You can generate an MCP server for Highlevel either through the Truto UI or programmatically via the API.

Method 1: Via the Truto UI

If you are configuring this for internal team use, the Truto dashboard is the fastest route:

1. Log in to your Truto environment.
2. Navigate to the Integrated Accounts page and select your connected Highlevel account.
3. Click the MCP Servers tab.
4. Click Create MCP Server.
5. Select your desired configuration (e.g., name the server, restrict allowed methods to "read" only, or filter by specific tags).
6. Copy the generated MCP server URL. It will look like https://api.truto.one/mcp/a1b2c3d4e5f6...

Method 2: Via the Truto API

For platform teams embedding AI capabilities into their own SaaS products, you can generate MCP servers programmatically.

Make a POST request to /integrated-account/:id/mcp using your Truto API token. You can pass a config object to restrict exactly what the LLM is allowed to do.

curl -X POST https://api.truto.one/integrated-account/<highlevel_account_id>/mcp \
  -H "Authorization: Bearer <your_truto_api_token>" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "Highlevel Sales Agent MCP",
    "config": {
      "methods": ["read", "write", "custom"],
      "tags": ["crm", "sales"]
    }
  }'

The API securely provisions a token in our distributed key-value store and returns a ready-to-use URL:

{
  "id": "mcp_srv_987654321",
  "name": "Highlevel Sales Agent MCP",
  "config": { 
    "methods": ["read", "write", "custom"], 
    "tags": ["crm", "sales"] 
  },
  "expires_at": null,
  "url": "https://api.truto.one/mcp/a1b2c3d4e5f67890"
}

How to Connect the MCP Server to ChatGPT

Once you have your Truto MCP server URL, you must connect it to your ChatGPT interface. You can do this visually in the ChatGPT app, or via a local configuration file if you are running a headless agent.

Method A: Via the ChatGPT UI

OpenAI supports custom MCP connectors natively in their desktop and web clients (available for Pro, Plus, Team, and Enterprise accounts).

1. Open ChatGPT and navigate to Settings -> Apps -> Advanced settings.
2. Enable Developer mode (MCP support is gated behind this toggle).
3. Under the MCP servers or Custom connectors section, click Add new server.
4. Name: Enter a recognizable label like "Highlevel CRM".
5. Server URL: Paste the Truto MCP URL (https://api.truto.one/mcp/...).
6. Click Save.

ChatGPT will immediately ping the /initialize endpoint, complete the JSON-RPC handshake, and load all available Highlevel tools into its context window.

Method B: Via Manual Config File (SSE Client)

If you are using an agentic framework or a client that requires a raw configuration file (like Cursor or a LangChain script), you can configure the MCP server using Server-Sent Events (SSE).

Create or update your mcp_config.json (or framework equivalent) with the following parameters:

{
  "mcpServers": {
    "highlevel_truto": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "--url",
        "https://api.truto.one/mcp/a1b2c3d4e5f67890"
      ]
    }
  }
}

Because Truto embeds the authentication directly into the URL token, no additional environment variables or API keys are required in the client configuration, unless you specifically enabled require_api_token_auth.

Hero Tools for Highlevel

When you connect Highlevel via Truto, you expose dozens of proxy API endpoints as agent tools. Here are the highest-leverage tools your AI agent can use to automate sales pipelines.

list_all_highlevel_search_contacts

This tool allows the LLM to search for contacts using advanced filters. It is critical for finding existing leads before creating duplicates. It returns comprehensive contact data including custom fields, tags, and DND (Do Not Disturb) status.

"Search Highlevel for any contact with the email address 'ceo@acmecorp.com' or the business name 'Acme Corp'."

create_a_highlevel_opportunity

Creates a new deal in a specific sales pipeline. The LLM must supply the pipelineId, pipelineStageId, contact details, and monetary value. This is the core engine for automated lead generation.

"Create a new opportunity for John Smith in the 'Inbound Leads' pipeline under the 'New Lead' stage. Set the monetary value to $2,500 and assign it to user ID 102."

highlevel_opportunities_status

Updates the status of an existing opportunity. The status must be strictly set to 'open', 'won', 'lost', or 'abandoned'. This tool prevents the LLM from having to rewrite the entire opportunity record just to close a deal.

"Find the opportunity ID for the 'Acme Corp Upgrade' deal and update its status to 'won'."

list_all_highlevel_conversations

Fetches recent communication threads for a specific location or contact. This tool pulls SMS, email, and live chat history, giving the LLM the context it needs to draft highly relevant follow-ups.

"Retrieve the last 5 conversations for contact ID 8899. Summarize what they were asking about regarding our pricing plans."

create_a_highlevel_conversation_message

Allows the LLM to send a new outbound message (Email or SMS) or log an inbound message to a conversation thread. It requires a conversationId and the specific message content.

"Draft a polite follow-up email thanking Sarah for her time on yesterday's demo, and send it directly to her active Highlevel conversation thread."

create_a_highlevel_calendar_event

Books an appointment on a specific Highlevel calendar. The LLM must specify the calendarId, contactId, and exact start and end times in ISO format.

"Book a 30-minute discovery call with Michael Scott on the 'Sales Main Calendar' for tomorrow at 2:00 PM EST."

To view the complete schema definitions and the full inventory of available Highlevel tools, visit the Highlevel integration page.

Workflows in Action

Connecting these tools transforms ChatGPT from a passive text generator into an active CRM operator. Here is how a sales team can orchestrate multi-step workflows.

Workflow 1: Inbound Lead Qualification

User Prompt:

"Check if Jane Doe (jane@example.com) is in Highlevel. If she is, read our last conversation thread. If she explicitly asked for pricing, create an opportunity worth $1,500 in the 'Main Pipeline' and text her back saying an account executive will call her today."

Execution Steps:

1. list_all_highlevel_search_contacts: ChatGPT searches for Jane Doe's email to retrieve her contactId and locationId.
2. list_all_highlevel_conversations: Using the contactId, the agent fetches her recent text and email history.
3. Analysis Phase: The LLM reads the payload, confirms the intent matches "asking for pricing", and decides to proceed.
4. create_a_highlevel_opportunity: The agent creates the deal, injecting the $1,500 monetary value and linking it to her contact record.
5. create_a_highlevel_conversation_message: The agent crafts the SMS and dispatches it to her active thread.

Outcome: The CRM is updated, the pipeline reflects new potential revenue, and the lead receives immediate engagement, all from a single conversational prompt.

Workflow 2: Stale Pipeline Cleanup

User Prompt:

"Find all opportunities assigned to me that have been in the 'Demo Scheduled' stage for more than 14 days. Draft a re-engagement email for each, send it, and move their status to 'abandoned'."

Execution Steps:

1. list_all_highlevel_opportunities: ChatGPT queries the opportunities endpoint, filtering by the user's ID and the specific pipeline stage, and calculates the date deltas.
2. create_a_highlevel_conversation_message: For each stale opportunity, the agent generates a personalized re-engagement email based on their previous notes, and sends it via the conversation API.
3. highlevel_opportunities_status: The agent loops through the identified deals and updates their status to 'abandoned' to clean up the forecast.

Outcome: The sales rep's pipeline is sanitized, and dormant leads are engaged simultaneously without manual data entry.

Security and Access Control

Giving an LLM write access to a production CRM is a massive security risk. Truto's MCP servers are designed with strict access controls enforced at the token level, guaranteeing the model cannot exceed its authorized boundaries.

• Method Filtering: You can explicitly restrict a server to read operations. If an LLM hallucinates a create_a_highlevel_opportunity tool call on a read-only server, Truto intercepts and blocks the execution.
• Tag Filtering: You can restrict tools by functional area. By configuring the server with tags: ["crm"], you prevent the LLM from accidentally accessing billing or saas_locations endpoints.
• Extra Authentication (require_api_token_auth): By default, possessing the MCP URL grants access. By setting this flag to true, the MCP client must also pass a valid Truto API token in the Authorization header. This ensures that even if the URL leaks in a server log, the tools cannot be called by unauthorized actors.
• Time-to-Live (expires_at): You can generate ephemeral MCP servers. Set an ISO timestamp, and Truto's distributed alarm system will automatically permanently delete the token and tear down the server at the exact expiration time.
• Account Isolation: Every MCP server is cryptographically bound to a single Highlevel integrated_account_id. The LLM physically cannot cross-pollinate data between different agencies or locations connected to your platform.

Reclaiming Engineering Velocity

Building AI agents that reliably operate against the Highlevel API requires more than just parsing standard JSON. You have to handle deeply nested pipeline structures, strict location contexts, and aggressive rate limits.

Instead of losing engineering cycles to token refreshes, schema maintenance, and writing boilerplate CRUD wrappers, you can use Truto to auto-generate production-ready MCP servers in seconds. Your engineers can focus on building better AI reasoning loops, while Truto handles the underlying integration complexity.