Connect Granola to ChatGPT: Search Meeting Notes and Manage Webhooks

If you need to connect Granola to ChatGPT to automate meeting summaries, extract insights from sales transcripts, or orchestrate webhooks across your organization, you need a Model Context Protocol (MCP) server. This server acts as the translation layer between ChatGPT's native tool calls and Granola'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 Granola to Claude or explore our broader architectural overview on connecting Granola to AI Agents.

Giving a Large Language Model (LLM) read and write access to a transcription and note-taking platform like Granola presents unique engineering challenges. You have to handle massive transcript payloads without blowing up the model's context window, manage cursor-based pagination, and secure webhook signing secrets. Every time you want to expose a new Granola capability to ChatGPT, a custom server requires you to manually write and deploy new JSON schemas.

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

Stop writing boilerplate API integration code. Let Truto generate secure, managed MCP servers for your AI agents in seconds. :::

The Engineering Reality of the Granola API

A custom MCP server is a self-hosted integration layer that sits between the LLM and the upstream API. While the open MCP standard provides a predictable way for models to discover tools, the reality of implementing it against specific vendor APIs is painful.

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

Transcript Payload Bloat and Context Windows

Granola is designed to capture and transcribe entire meetings. A single get request for a Granola note can return an enormous text payload. If an LLM naively requests ten meeting notes at once and the API returns the raw transcript for all of them, you will instantly blow past your model's token limits and increase inference costs exponentially. A properly engineered Granola tool must explicitly split operations: you provide tools to fetch the lightweight summary_markdown first, and require the LLM to make a secondary, targeted call if it absolutely needs the verbatim transcript string.

Ephemeral Webhook Signing Secrets

When managing Granola webhooks via API, the create endpoint returns a signing_secret. This secret is returned exactly once in the response body of the creation call. If your custom MCP server doesn't have a mechanism to immediately capture and route that secret back to the user or a secure vault, the webhook is effectively useless because you cannot verify incoming event payloads. Managing this state via an LLM tool call requires precise schema definitions and explicit prompt instructions.

HTTP 429 Errors and Rate Limit Pass-Through

Granola enforces rate limits to protect its infrastructure, especially when querying heavy transcript data. A critical architectural detail: Truto does not retry, throttle, or apply backoff on rate limit errors. When the upstream Granola API returns an HTTP 429 (Too Many Requests), Truto passes that error directly back to the caller. Truto normalizes the upstream rate limit information into standardized headers (ratelimit-limit, ratelimit-remaining, ratelimit-reset) per the IETF specification. Your custom MCP client or the AI agent itself is entirely responsible for reading these headers and executing exponential backoff. If you fail to build this into your client logic, the LLM will hallucinate successful operations when the API is actually blocking the requests.

Generating the Granola MCP Server

Instead of forcing your engineering team to build a Node.js or Python server from scratch, manage OAuth tokens, and write massive JSON schemas for Granola's endpoints, Truto generates a secure MCP server dynamically.

Tools are derived directly from Granola's API documentation and your environment's integration configuration. There are two ways to generate this server.

Method 1: Via the Truto UI

For IT admins and operators, the easiest way to generate a server is through the Truto dashboard.

1. Navigate to the Integrated Accounts page for your connected Granola instance.
2. Click the MCP Servers tab.
3. Click Create MCP Server.
4. Select your desired configuration (e.g., restrict to read methods only, or filter by specific tags).
5. Copy the generated MCP server URL (e.g., https://api.truto.one/mcp/a1b2c3d4e5f6...).

Method 2: Via the Truto API

For developers building programmatic agent workflows, you can generate the MCP server dynamically via the Truto REST API. This validates that the Granola integration has available tools, generates a secure hashed token, and returns a ready-to-use URL.

Request:

curl -X POST https://api.truto.one/integrated-account/{integrated_account_id}/mcp \
  -H "Authorization: Bearer YOUR_TRUTO_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "name": "ChatGPT Granola Server",
    "config": {
      "methods": ["read", "write"]
    }
  }'

Response:

{
  "id": "mcp_8a7b6c5d",
  "name": "ChatGPT Granola Server",
  "config": { "methods": ["read", "write"] },
  "expires_at": null,
  "url": "https://api.truto.one/mcp/a1b2c3d4e5f6..."
}

Connecting the MCP Server to ChatGPT

Once you have your Truto MCP URL, connecting it to your AI agent requires zero additional code. The URL contains a cryptographic token that securely identifies the integrated account and configuration.

Via the ChatGPT UI

If you are using the ChatGPT desktop app (macOS/Windows) with an eligible account (Pro, Plus, Team, Enterprise):

1. Open ChatGPT and navigate to Settings -> Apps -> Advanced settings.
2. Enable the Developer mode toggle.
3. Under MCP servers / Custom connectors, click to add a new server.
4. Name: Granola MCP
5. Server URL: Paste the Truto MCP URL (https://api.truto.one/mcp/...).
6. Click Save. ChatGPT will immediately handshake with the server and discover the Granola tools.

Via Manual Configuration File (SSE)

If you are building a custom LangChain agent, Cursor, or deploying a headless MCP client, you can connect using the Server-Sent Events (SSE) transport. Configure your MCP client to point to the Truto URL.

{
  "mcpServers": {
    "granola": {
      "command": "npx",
      "args": [
        "@modelcontextprotocol/server-sse",
        "--url",
        "https://api.truto.one/mcp/a1b2c3d4e5f6..."
      ]
    }
  }
}

Granola Hero Tools for ChatGPT

Truto dynamically generates MCP tools based on the Granola API schema. Here are the highest-leverage tools your ChatGPT agent can use to orchestrate meeting notes and webhooks.

List All Granola Notes

Retrieves a paginated list of Granola notes, optionally filtered by date or folder. This tool explicitly instructs the LLM on how to handle cursor-based pagination so it can traverse large histories without getting stuck in a loop. It returns metadata like id, title, owner, and updated_at, but excludes the heavy transcript data to preserve context.

"Find all meeting notes in the 'Q3 Sales' folder created after October 1st. If there are more than 30, use the cursor to fetch the next page."

Get Single Granola Note by ID

Fetches the complete details of a specific meeting note. This is the primary tool for extracting meeting intelligence. It returns the summary_text, calendar_event details, and attendees. You can explicitly instruct the LLM to request the transcript boolean if verbatim quotes are required.

"Get the full details for the note ID 'note_9876xyz'. I need the markdown summary and the list of attendees. Do not pull the full transcript yet."

List All Granola Folders

Allows the AI agent to navigate the organizational structure of your Granola workspace. This is critical for workflows that need to locate specific departmental notes or apply webhooks to specific sub-directories.

"List all the folders in our Granola workspace and find the exact ID for the folder named 'Engineering Standups'."

List All Granola Webhook Endpoints

Audits the current webhook configurations that your API key is authorized to manage. This tool allows ChatGPT to verify if an external system (like a CRM or Slack bot) is already receiving real-time meeting updates before attempting to create a duplicate webhook.

"Check our Granola workspace to see if we have any active webhooks pointing to the domain 'hooks.slack.com'."

Create a Granola Webhook Endpoint

Provisions a new event listener for real-time meeting intelligence. The agent can specify the destination url and the scopes (events) it wants to subscribe to. The LLM must be instructed to output the signing_secret returned by this tool, as it will never be accessible again.

"Create a new Granola webhook endpoint pointing to 'https://api.ourcompany.com/granola-ingest'. Subscribe it to note creation events. Print the signing secret clearly so I can save it to our vault."

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

Workflows in Action

Connecting Granola to ChatGPT via MCP allows you to move beyond simple chat queries and build autonomous workflows that chain multiple API calls together. Here is how a custom GPT or AI agent executes complex tasks.

Workflow 1: Sales Discovery Meeting Prep and Follow-up

Account executives often need to recall historical context before a follow-up call, but reading through raw transcripts takes too much time. You can instruct ChatGPT to isolate specific themes from past meetings.

"Find the most recent meeting note in the 'Enterprise Sales' folder about Acme Corp. Summarize the next steps, and if the client mentioned 'security requirements', pull the full transcript for that specific note to extract their exact phrasing."

Execution Steps:

1. The agent calls list_all_granola_folders to find the ID for the "Enterprise Sales" folder.
2. The agent calls list_all_granola_notes passing the folder ID as a query parameter to locate the Acme Corp meeting.
3. The agent calls get_single_granola_note_by_id requesting the summary_markdown to check for next steps and mentions of security.
4. Realizing security was discussed, the agent calls get_single_granola_note_by_id again, this time with the transcript: true parameter, to extract the exact verbatim quotes.

sequenceDiagram
    participant User as User Prompt
    participant ChatGPT as ChatGPT
    participant Truto as Truto MCP Server
    participant Granola as Granola API

    User->>ChatGPT: "Find Acme Corp meeting in Enterprise Sales..."
    ChatGPT->>Truto: tools/call (list_all_granola_folders)
    Truto->>Granola: GET /folders
    Granola-->>Truto: Return folder ID
    Truto-->>ChatGPT: Folder ID: fld_123
    ChatGPT->>Truto: tools/call (list_all_granola_notes, folder: fld_123)
    Truto->>Granola: GET /notes?folder=fld_123
    Granola-->>Truto: Return note metadata
    Truto-->>ChatGPT: Note ID: note_456
    ChatGPT->>Truto: tools/call (get_single_granola_note_by_id, id: note_456)
    Truto->>Granola: GET /notes/note_456
    Granola-->>Truto: Return summary_markdown
    Truto-->>ChatGPT: Summary data

Workflow 2: Automated Integration Sync via Webhooks

DevOps teams frequently need to wire up internal tools to Granola events. Instead of clicking through UI menus, an admin can ask ChatGPT to audit and provision the infrastructure.

"Audit our Granola webhooks. If there isn't a webhook configured for the 'Product Feedback' folder sending data to 'https://internal.app/granola', create one. Make sure it's enabled and subscribe it to note updates."

Execution Steps:

1. The agent calls list_all_granola_folders to resolve the ID for "Product Feedback".
2. The agent calls list_all_granola_webhook_endpoints to audit the existing hooks.
3. The agent parses the array of webhooks to see if the target URL exists for that specific folder ID.
4. Finding no match, the agent calls create_a_granola_webhook_endpoint with the target URL, the required scopes, and the folder filter.
5. The agent formats the output, explicitly presenting the one-time signing_secret to the user.

flowchart TD
    A["Audit Webhooks<br>(list_all)"] --> B{"Target URL<br>Exists?"}
    B -- Yes --> C["Verify Scopes<br>and Status"]
    B -- No --> D["Create Webhook<br>(create_endpoint)"]
    D --> E["Extract and Display<br>signing_secret"]

Security and Access Control

Giving an LLM access to your company's internal meeting transcripts requires strict security boundaries. Truto's MCP servers provide multiple layers of access control out of the box, ensuring the agent can only do exactly what you authorize.

• Method Filtering (config.methods): You can restrict an MCP server to strictly read-only operations. By setting methods: ["read"], Truto will automatically filter out tools like create_a_granola_webhook_endpoint or delete_a_granola_webhook_endpoint_by_id. The LLM physically cannot modify infrastructure.
• Tag Filtering (config.tags): If you only want the AI to access webhooks and not notes, you can filter tools by their assigned resource tags (e.g., tags: ["webhooks"]).
• API Token Authentication (require_api_token_auth): By default, possessing the Truto MCP URL grants access to the tools. For enterprise deployments, setting this flag to true forces the client to also pass a valid Truto API token in the Authorization header. This validates the identity of the user executing the tool call.
• Automatic Expiry (expires_at): For short-lived audit tasks, you can generate an MCP server with a TTL (Time-To-Live). Once the ISO datetime is reached, Truto automatically deletes the server configuration and KV records, severing the LLM's access to Granola.

The Strategic Advantage of Managed MCP

Building a custom integration between ChatGPT and Granola is a massive distraction from your core product. You have to handle OAuth token refreshes, write complex pagination logic, parse HTTP 429 rate limit headers, and manually update JSON schemas every time Granola ships a new API feature.

Truto's dynamic, documentation-driven MCP architecture eliminates this technical debt entirely. By deriving tools directly from the API specification and handling the JSON-RPC protocol natively, Truto allows your engineering team to connect AI agents to Granola in minutes, not months. You get secure, highly-scoped tool calling with zero infrastructure to maintain.

Stop writing boilerplate. Let your AI agents securely access Granola data the right way.