Connect Warp to ChatGPT: Run Agents and Monitor Transcripts

If you need to connect Warp to ChatGPT to orchestrate agent runs, debug transcripts, or monitor self-hosted workers, you need a Model Context Protocol (MCP) server. This server acts as the translation layer between ChatGPT's tool calls and Warp's REST APIs. You can either build and maintain this infrastructure yourself, or use a managed integration platform like Truto to dynamically generate a secure, authenticated MCP server URL. If your team uses Claude, check out our guide on connecting Warp to Claude or explore our broader architectural overview on connecting Warp to AI Agents.

Giving a Large Language Model (LLM) read and write access to an agent orchestration platform like Warp is an engineering challenge. You have to handle long-running asynchronous states, map nested JSON schemas to MCP tool definitions, and deal with complex file redirects. Every time Warp updates an endpoint, you have to update your server code, redeploy, and test the integration. This guide breaks down exactly how to use Truto to generate a secure, managed MCP server for Warp, connect it natively to ChatGPT, and execute complex workflows using natural language.

The Engineering Reality of the Warp API

A custom MCP server is a self-hosted integration layer. While the open MCP standard provides a predictable way for models to discover tools, the reality of implementing it against Warp's APIs - or maintaining custom connectors for 100+ other platforms - is painful. You are not just integrating a simple database; you are integrating an asynchronous orchestration engine.

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

Handling 302 Redirects for Transcripts and Artifacts

The Warp API does not just return raw text strings when you request a massive agent transcript or file artifact. Tools like list_all_warp_run_transcripts and get_single_warp_agent_artifact_by_id often issue a 302 redirect to a time-limited signed download URL. Standard HTTP clients inside naive AI agents get confused by this or drop the payload entirely. Your MCP server must be intelligent enough to follow the redirect, fetch the raw file content from the signed URL, and return it to the LLM in a structured JSON-RPC text block, all without holding massive files in memory.

Asynchronous State Management and Timelines

Warp agents do not run instantaneously. When you call create_a_warp_agent_run, the API spawns an agent but returns a pending or queued state. The HTTP request closes. If your AI agent assumes the task is done, it will hallucinate the result. Your MCP implementation requires explicitly defined tools for polling get_single_warp_agent_run_by_id or list_all_warp_run_timelines so the LLM knows how to check the state, wait, and verify execution success before proceeding to the next chain of thought.

Rate Limits and 429 Transparency

Warp enforces rate limits to prevent runaway loops from exhausting compute resources. It is critical to understand that Truto does not absorb, retry, or apply exponential backoff to rate limit errors. When the Warp API rejects a request with an HTTP 429 Too Many Requests, Truto passes that exact error directly back to the LLM client.

What Truto does provide is normalization. Truto intercepts the upstream rate limit data and standardizes it into ratelimit-limit, ratelimit-remaining, and ratelimit-reset headers per the IETF specification. Your LLM framework or the LLM itself is responsible for reading these headers and executing the retry logic. If you build your own server, you must parse varying rate limit headers and ensure you do not crash the MCP connection when the LLM triggers a 429.

Step 1: Generating the MCP Server for Warp

Instead of writing protocol handlers, you can generate an MCP server dynamically using Truto. Truto derives the tools directly from Warp's API documentation and your environment's integration configuration. There are two ways to do this.

Method 1: Via the Truto UI

The fastest way to provision a Warp MCP server is through the dashboard. This generates a secure, tokenized URL scoped specifically to your connected Warp workspace.

1. Log into Truto and navigate to the integrated account page for your Warp connection.
2. Click the MCP Servers tab.
3. Click Create MCP Server.
4. Define your configuration parameters (name, method restrictions, tags, and optional expiration dates).
5. Copy the generated MCP server URL (e.g., https://api.truto.one/mcp/abc123def456...).

Method 2: Via the Truto API

For platform engineers who want to provision MCP servers programmatically for their end users, use the /mcp REST endpoint. This requires a valid Truto API token.

Request:

POST /integrated-account/{integrated_account_id}/mcp
Authorization: Bearer <TRUTO_API_KEY>
Content-Type: application/json
 
{
  "name": "Warp Orchestrator MCP",
  "config": {
    "methods": ["read", "write", "custom"]
  },
  "expires_at": "2026-12-31T23:59:59Z"
}

Response:

{
  "id": "mcp_8f7d6c5b4a",
  "name": "Warp Orchestrator MCP",
  "config": {
    "methods": ["read", "write", "custom"]
  },
  "expires_at": "2026-12-31T23:59:59.000Z",
  "url": "https://api.truto.one/mcp/a1b2c3d4e5f6g7h8i9j0..."
}

This tokenized URL is backed by edge storage and handles all JSON-RPC 2.0 protocol handshakes. The tools are derived dynamically - if a Warp endpoint lacks documentation, it is skipped, acting as a quality gate to prevent LLM hallucinations on undocumented parameters. Truto flattens the query and body parameters into a single input namespace, routing the LLM's arguments to the correct API payload automatically.

Step 2: Connecting the MCP Server to ChatGPT

Once you have your Truto MCP URL, you need to connect it to your LLM environment. You can do this directly in the ChatGPT interface or via a local configuration file if you are running custom agents.

Method A: Via the ChatGPT UI

ChatGPT supports custom connectors via remote MCP for advanced tiers.

1. Open ChatGPT and navigate to Settings.
2. Go to Apps - Advanced settings.
3. Toggle on Developer mode (MCP support requires this flag to be active).
4. Under the MCP servers / Custom connectors section, click Add new server.
5. Set the Name to something recognizable, like Warp Orchestrator.
6. Paste your Truto MCP URL into the Server URL field.
7. Click Save.

ChatGPT will immediately send an initialize JSON-RPC request to the URL, discover the available Warp tools, and make them available in your chat context.

Method B: Via Manual Configuration File

If you are running an AI framework (like LangChain, Cursor, or Claude Desktop) or building a custom agent wrapper around ChatGPT's API, you can connect using the standard server-sse transport.

Create or update your mcp.json or claude_desktop_config.json file:

{
  "mcpServers": {
    "warp_integration": {
      "command": "npx",
      "args": [
        "-y",
        "@modelcontextprotocol/server-sse",
        "https://api.truto.one/mcp/a1b2c3d4e5f6g7h8i9j0..."
      ]
    }
  }
}

Restart your agent client. It will use the Server-Sent Events bridge to communicate with Truto's HTTP endpoint.

Hero Tools for Warp

Truto exposes the entirety of the Warp API, but LLMs perform best when orchestrating high-leverage operations. Here are the hero tools you should prioritize when instructing your agent.

create_a_warp_agent_run

This is the core execution tool. It spawns a Warp cloud agent run with a specific prompt. It queues the agent for execution and returns a unique run_id.

Contextual note: This tool does not wait for the agent to finish. The LLM must capture the run_id and use subsequent tools to check the status.

"Deploy an agent run in Warp with the prompt: 'Scrape the pricing page of competitor X and summarize the tiers.' Give me the run_id once it is queued."

get_single_warp_agent_run_by_id

Retrieves detailed information about a specific agent run, including its current state (e.g., PENDING, RUNNING, SUCCEEDED, FAILED), prompt, execution location, and request usage.

Contextual note: Instruct your LLM to poll this tool with a delay if the state is not terminal.

"Check the status of Warp run_id 'run_9x8y7z'. If it is SUCCEEDED, tell me the run time. If it failed, extract the status_message."

list_all_warp_run_transcripts

Fetches the raw conversation transcript for a completed agent run. This handles the 302 redirect abstraction layer natively.

Contextual note: Transcripts can be large. Ask your LLM to summarize specific sections rather than printing the entire JSON block to the user.

"Get the full run transcript for run_id 'run_9x8y7z' and summarize exactly what the agent discovered during its final execution step."

list_all_warp_agent_schedules

Lists all scheduled agents (cron jobs) accessible to the authenticated user. Returns details on enabled status, cron expressions, and the next scheduled execution time.

Contextual note: Extremely useful for IT admins trying to audit rogue or failing scheduled tasks across environments.

"List all Warp agent schedules. Identify any schedules that have a 'last_spawn_error' and summarize what went wrong for each."

create_a_warp_run_followup

Submits a follow-up message to an existing Warp agent run. This routes the message transparently based on whether the run is queued, active, or ended.

Contextual note: Use this to provide human-in-the-loop feedback or correct an agent mid-flight.

"Send a followup message to run_id 'run_9x8y7z' telling the agent to ignore the staging environment and focus only on production."

list_all_warp_agent_connected_self_hosted_workers

Lists currently connected self-hosted workers for your team. It returns connection counts and heartbeat timestamps.

Contextual note: Essential for DevOps observability. If an agent run is stuck in PENDING for a self-hosted skill, this tool verifies if the worker is actually alive.

"Check if we have any active self-hosted workers connected to Warp. Tell me their host names and when they were last seen."

For the complete inventory of available endpoints and their exact JSON schemas, visit the Warp integration page.

Workflows in Action

Connecting tools is only half the battle. Here is how specific personas use these tools in chained sequences to automate complex orchestration tasks.

Scenario 1: The Asynchronous Agent Orchestrator

A DevOps engineer needs to run a specialized security audit agent across a staging environment and parse the results.

"Spawn a new Warp agent run to 'Execute the security vulnerability audit on the staging cluster'. Monitor the run until it finishes, then download the transcript and summarize all critical CVEs found."

Step-by-step execution:

1. The LLM calls create_a_warp_agent_run with the prompt payload. It receives run_id: 885522 in state PENDING.
2. The LLM calls get_single_warp_agent_run_by_id passing 885522. The state is RUNNING.
3. The LLM waits, then calls get_single_warp_agent_run_by_id again. The state is SUCCEEDED.
4. The LLM calls list_all_warp_run_transcripts passing 885522 to download the raw JSON transcript.
5. The LLM parses the transcript internally and outputs a clean markdown summary of the CVEs to the user.

sequenceDiagram
    participant User as User
    participant ChatGPT as ChatGPT
    participant Truto as Truto MCP
    participant Warp as Warp API
    User->>ChatGPT: Run audit agent and summarize CVEs
    ChatGPT->>Truto: call create_a_warp_agent_run
    Truto->>Warp: POST /v1/runs
    Warp-->>Truto: run_id: 885522, state: PENDING
    Truto-->>ChatGPT: run_id: 885522
    ChatGPT->>Truto: call get_single_warp_agent_run_by_id
    Truto->>Warp: GET /v1/runs/885522
    Warp-->>Truto: state: SUCCEEDED
    Truto-->>ChatGPT: state: SUCCEEDED
    ChatGPT->>Truto: call list_all_warp_run_transcripts
    Truto->>Warp: GET /v1/runs/885522/transcript
    Warp-->>Truto: 302 Redirect to signed URL -> Fetch Data
    Truto-->>ChatGPT: Raw transcript data
    ChatGPT-->>User: Here is your CVE summary...

Scenario 2: The Infrastructure Health Auditor

An IT administrator notices that scheduled tasks are silently failing and needs to diagnose the worker fleet.

"Find all active self-hosted Warp workers. Then check all scheduled agents to see if any have failed recently. If you find a failed schedule, get the details of the failure and tell me if it correlates with a missing worker."

Step-by-step execution:

1. The LLM calls list_all_warp_agent_connected_self_hosted_workers to map available infrastructure.
2. The LLM calls list_all_warp_agent_schedules and scans the returned array for last_spawn_error.
3. It identifies schedule sch_4411 has an error.
4. The LLM calls get_single_warp_agent_schedule_by_id on sch_4411 to inspect the agent_config and environment.
5. The LLM cross-references the schedule's required execution environment with the worker list from step 1, concluding that the necessary worker host is offline, and alerts the user.

Security and Access Control

Exposing an orchestration engine like Warp to an AI model requires strict governance. Truto MCP servers provide multiple layers of access control configured at creation time:

• Method Filtering: Restrict the MCP server to specific HTTP methods. By setting methods: ["read"], the LLM can only query states and transcripts (e.g., get, list) but cannot invoke create_a_warp_agent_run or delete_a_warp_agent_identity_by_id.
• Tag Filtering: Group tools functionally. You can restrict the server to only expose tools tagged with monitoring, keeping the LLM entirely isolated from identity administration tools.
• API Token Authentication: By default, possessing the MCP URL is enough to connect. By setting require_api_token_auth: true, Truto forces the client to pass a valid Truto API token in the Authorization header, ensuring only authenticated personnel can utilize the server.
• Time-to-Live (TTL): Use the expires_at property to grant an LLM temporary access to Warp. Once the timestamp is reached, distributed state alarms trigger an automated cleanup, completely revoking access.

Connecting Warp to ChatGPT via MCP transforms how engineering teams handle AI orchestration. By offloading the API boilerplate, rate limit normalization, and file redirect logic to Truto, you can focus on building intelligent, multi-step agent architectures.