Connect Apono to ChatGPT: Manage Access via MCP

Managing just-in-time (JIT) access and permissions at scale usually results in IT teams drowning in Jira tickets. Engineers need temporary database access, compliance teams require immediate audit logs, and DevOps needs to rotate infrastructure privileges. You want to connect Apono to ChatGPT so your AI agents can provision access, audit roles, and query active sessions entirely through natural language.

To connect Apono to ChatGPT, you need a Model Context Protocol (MCP) server. This server acts as a translation layer, converting an LLM's standardized tool calls into Apono's specific REST API requests. By using a managed MCP server, you bypass the boilerplate of authentication management, JSON schema mapping, and rate limit header normalization.

Giving a Large Language Model (LLM) read and write access to your infrastructure security platform is a serious engineering task. You either spend weeks building, hosting, and maintaining a custom MCP server, or you use a managed infrastructure layer that handles the protocol dynamically. This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for Apono, connect it natively to ChatGPT, and execute complex access workflows using natural language.

The Engineering Reality of Custom Apono Connectors

A custom MCP server is a self-hosted integration layer. While the Model Context Protocol provides a predictable way for models to discover tools, implementing it against vendor APIs takes significant engineering effort.

If you decide to build a custom MCP server for Apono, you own the entire API lifecycle. Apono's REST API manages highly sensitive infrastructure access. Mapping this to an LLM requires strict schema definitions. Every time you want to expose a new Apono endpoint - like listing access bundles or updating a connector - you have to hand-code the tool definition, define the query parameters, and map the response payload back into a format the LLM can understand.

Then there is authentication. Your server must securely store Apono API tokens, handle token expiration, and ensure that the LLM only executes actions within the scoped permissions of that token.

How Truto's Managed MCP Server Works

Instead of hand-coding tool definitions, Truto's MCP servers are dynamic and documentation-driven. When you connect an Apono account, Truto reads the integration's internal API definitions and automatically generates a set of MCP tools.

Each MCP server is scoped to a single connected Apono account. The server URL contains a cryptographic token that encodes which account to use and what tools to expose. The URL alone authenticates the client, meaning no additional configuration is needed inside ChatGPT.

Handling Apono Rate Limits

When exposing APIs to autonomous agents, rate limiting becomes a primary concern. LLMs can easily trigger pagination loops or aggressive polling that hits Apono's API limits.

Truto does not automatically retry, throttle, or apply backoff on rate limit errors. If your agent hits a 429 Too Many Requests error, Truto passes that error directly back to the caller.

What Truto does do is normalize the rate limit information from Apono into standardized response headers based on the IETF specification:

• ratelimit-limit: Maximum requests allowed in the current window.
• ratelimit-remaining: Requests left in the current window.
• ratelimit-reset: Seconds until the window resets.

Your agent framework (whether you are using LangChain, LangGraph, or custom application logic) is responsible for reading these standardized headers and implementing its own exponential backoff logic.

Step-by-Step: Creating the Apono MCP Server

You can generate an MCP server for Apono using either the Truto UI or the REST API. Both methods yield a secure JSON-RPC 2.0 endpoint that ChatGPT can consume.

Method 1: Via the Truto UI

This is the fastest method for internal testing and manual agent configuration.

1. Navigate to the integrated account page for your Apono connection in the Truto dashboard.
2. Click the MCP Servers tab.
3. Click Create MCP Server.
4. Select your desired configuration. You can filter tools by methods (e.g., only allow read operations to prevent the AI from modifying access flows) or apply tags.
5. Copy the generated MCP server URL.

Method 2: Via the Truto API

For teams building programmatic AI workflows, you can generate MCP servers dynamically. This is useful if you want to spin up a temporary MCP server with a strict Time-to-Live (TTL) for a specific agent task.

Make a POST request to /integrated-account/:id/mcp:

{
  "name": "Apono Read-Only Auditor",
  "config": {
    "methods": ["read"]
  },
  "expires_at": "2026-10-01T12:00:00Z"
}

The API returns the database record along with the ready-to-use URL:

{
  "id": "mcp_abc123",
  "name": "Apono Read-Only Auditor",
  "config": { "methods": ["read"] },
  "expires_at": "2026-10-01T12:00:00Z",
  "url": "https://api.truto.one/mcp/a1b2c3d4e5f6..."
}

Connecting the MCP Server to ChatGPT

Once you have the Truto MCP URL, connecting it to ChatGPT takes under a minute.

1. Open ChatGPT and navigate to Settings > Apps > Advanced settings.
2. Enable Developer mode (MCP support requires this flag).
3. Under MCP servers / Custom connectors, click to add a new server.
4. Enter a name (e.g., "Apono JIT Access").
5. Paste the Truto MCP server URL into the Server URL field.
6. Save the configuration.

ChatGPT will immediately perform a handshake with the server, requesting the tools/list endpoint. Truto dynamically compiles the Apono schemas and returns the available tools.

Apono MCP Tools Inventory

Truto exposes a comprehensive suite of Apono endpoints as standardized MCP tools. Your LLM can use these tools to execute complex infrastructure access workflows.

Below is the complete inventory of Apono tools available. For the most up-to-date documentation and schema details, visit the Apono integration page.

Users, Groups, and Identities

• list_all_apono_users: List users in Apono. Returns id, email, first_name, last_name, active status, roles, and source integrations.
• get_single_apono_user_by_id: Get information about a specific user in Apono using their id.
• list_all_apono_identities: List attributes for multiple identities in Apono. Returns each identity's email and associated attributes.
• list_all_apono_groups: List groups in Apono. Returns an array of groups with fields id, name, source_id, and source_integration_id.
• get_single_apono_group_by_id: Get details about a specific group in Apono using its id.
• create_a_apono_group: Create a group in Apono by providing a name and members_emails.
• delete_a_apono_group_by_id: Delete a specific group in Apono using its id.

Access Scopes & Bundles

• list_all_apono_access_scopes: List access scopes in Apono. Returns access scopes with fields id, name, query, and timestamps.
• get_single_apono_access_scope_by_id: Get details of a specific access scope.
• create_a_apono_access_scope: Create an access scope in Apono using a name and query.
• update_a_apono_access_scope_by_id: Update an existing access scope.
• delete_a_apono_access_scope_by_id: Delete an access scope.
• list_all_apono_access_bundles: List access bundles in Apono. Returns each bundle with id, name, and details about integration targets.
• get_single_apono_access_bundle_by_id: Get details of a specific access bundle.
• create_a_apono_access_bundle: Create an access bundle. Requires name and integration_targets.
• update_a_apono_access_bundle_by_id: Update an existing access bundle.
• delete_a_apono_access_bundle_by_id: Delete an existing access bundle.
• list_all_apono_available_access_bundles: List available access bundles in Apono.
• list_all_apono_bundles: List general bundles in Apono.
• get_single_apono_bundle_by_id: Get details of a specific bundle.
• create_a_apono_bundle: Create a new bundle requiring a name and access_targets.
• update_a_apono_bundle_by_id: Update a specific bundle.
• delete_a_apono_bundle_by_id: Delete a specific bundle.

Access Flows, Requests, and Sessions

• list_all_apono_access_flows: List access flows in Apono. Returns id, name, active status, triggers, access targets, and approver policies.
• get_single_apono_access_flow_by_id: Get details of a specific access flow.
• create_a_apono_access_flow: Create an access flow. Requires name, active, trigger, requestors, access_targets, and settings.
• update_a_apono_access_flow_by_id: Update an existing access flow.
• delete_a_apono_access_flow_by_id: Delete an access flow.
• list_all_apono_delegated_access_requests: List delegated access requests. Returns id, status, duration_in_sec, justification, and creation details.
• get_single_apono_delegated_access_request_by_id: Get a specific delegated access request.
• list_all_apono_access_sessions: List access sessions in Apono. Returns id, name, request_ids, and integration details.
• get_single_apono_access_session_by_id: Get details of a specific access session.

Integrations & Connectors

• list_all_apono_integrations: List integrations in Apono. Returns fields such as id, name, type, category, and status.
• get_single_apono_integration_by_id: Get details about a specific integration.
• create_a_apono_integration: Create a new integration. Requires name and type.
• update_a_apono_integration_by_id: Update an existing integration.
• delete_a_apono_integration_by_id: Delete a specific integration.
• list_all_apono_connectors: List connectors in Apono. Returns id, name, status, version, and cloud details.
• get_single_apono_connector_by_id: Get details about a specific connector.
• update_a_apono_connector_by_id: Update a specific connector.
• delete_a_apono_connector_by_id: Delete a connector.

Reporting & Activity

• list_all_apono_activity: List activity records in Apono. Returns request_id, request_date, requestor_name, requestor_email, and more.
• list_all_apono_activity_reports: List activity reports. Returns id, name, filters, timeframe, schedule, and format details.
• get_single_apono_activity_report_by_id: Get a specific activity report.
• create_a_apono_activity_report: Create an activity report. Requires name, filters, and timeframe.
• update_a_apono_activity_report_by_id: Update an existing activity report.
• delete_a_apono_activity_report_by_id: Delete a specific activity report.
• list_all_apono_attributes: List attributes in Apono. Returns key fields including type, value, source_id, and source_integration_id.

Example Workflow: Automating JIT Access Audits

When ChatGPT is equipped with these tools, you can run complex, multi-step queries via natural language.

Imagine an IT admin asking ChatGPT: "Show me all active access flows related to production databases, and list the users currently assigned to those flows."

Behind the scenes, the LLM orchestrates the necessary API calls via the MCP server:

sequenceDiagram
    participant Admin
    participant ChatGPT
    participant TrutoMCP as Truto MCP Server
    participant AponoAPI as Apono API
    
    Admin->>ChatGPT: "Show active access flows for production databases."
    ChatGPT->>TrutoMCP: Call list_all_apono_access_flows
    TrutoMCP->>AponoAPI: GET /api/v1/access-flows
    AponoAPI-->>TrutoMCP: Array of Access Flows
    TrutoMCP-->>ChatGPT: JSON Response
    ChatGPT->>TrutoMCP: Call list_all_apono_users
    TrutoMCP->>AponoAPI: GET /api/v1/users
    AponoAPI-->>TrutoMCP: Array of Users
    TrutoMCP-->>ChatGPT: JSON Response
    ChatGPT-->>Admin: Synthesized list of active flows and authorized users.

The LLM parses the user array, cross-references the IDs against the access flow definitions, and returns a clean, conversational summary of exactly who has access to what.

Next Steps

Building AI agents that interact with infrastructure security tools requires reliable, strictly defined API access. Hand-coding custom MCP servers forces your engineering team to maintain OAuth flows, pagination logic, and schema definitions indefinitely (a challenge we also detailed in our guide to connecting Pylon to ChatGPT).

Using a managed MCP server offloads the integration boilerplate, allowing your team to focus on building the actual agent logic and prompt orchestration.