Granola connector for AI agents

Tools your Granola agent reaches for, scoped per user.

CALL ANY TOOL

Retrieve meeting notes, summaries, and attendee data. Same toolkit, every framework, no auth plumbing to maintain.

granola_notes_list

List notes

List all accessible meeting notes in the Granola workspace with pagination and date filtering.

Parameters

Name

Type

Required

Description

limit

integer

Optional

Max notes to return (default 20)

after

string

Optional

ISO 8601 date filter — return notes created after this date

before

string

Optional

ISO 8601 date filter — return notes created before this date

granola_note_get

Get note

granola_note_summary_get

Get note summary

granola_note_attendees_list

List attendees

Build your Agent

Drop the toolkit in, point it at the user, and your agent can retrieve meeting notes and summaries from the first run.

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import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["granola"], toolNames: ["granola_notes_list", "granola_note_get", "granola_note_summary_get"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: t.tool.definition.input_schema,
  func: async (args) => sk.tools.executeTool({
    toolName: t.tool.definition.name, identifier: "user_123", toolInput: args,
  }),
}));

const agent = createReactAgent({ llm, tools: lcTools });

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import { ScalekitClient } from "@scalekit-sdk/node";
import OpenAI from "openai";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const openai = new OpenAI();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["granola"], toolNames: ["granola_notes_list", "granola_note_get", "granola_note_summary_get"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  function: {
    name: t.tool.definition.name,
    description: t.tool.definition.description,
    parameters: t.tool.definition.input_schema,
  },
}));

const resp = await openai.responses.create({
  model: "gpt-4o", input: prompt, tools: llmTools,
});

import { ScalekitClient } from "@scalekit-sdk/node";
import Anthropic from "@anthropic-ai/sdk";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);
const anthropic = new Anthropic();

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["granola"], toolNames: ["granola_notes_list", "granola_note_get", "granola_note_summary_get"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  input_schema: t.tool.definition.input_schema,
}));

const msg = await anthropic.messages.create({
  model: "claude-sonnet-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

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import { Agent } from "@google/adk/agents";
import {
  MCPToolset, StreamableHTTPConnectionParams,
} from "@google/adk/tools/mcp";

const toolset = new MCPToolset({
  connectionParams: new StreamableHTTPConnectionParams({
    url: "https://mcp.scalekit.com/granola",
    headers: { Authorization: `Bearer ${userScopedToken}` },
  }),
});

const agent = new Agent({
  name: "agent", model: "gemini-2.0-flash",
  tools: await toolset.getTools(),
});

Try these prompts

Paste any prompt into your agent to start pulling meeting intelligence.

Search & recall

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Summarize what was decided in my last five meetings.

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What action items came out of [meeting name]?

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Find all meetings where [topic] was discussed.

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What did [person] say in yesterday's standup?

Action & follow-up

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List all open action items from meetings this week.

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Who attended the last product review meeting?

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Get the transcript from [meeting name].

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Summarize all meetings from [date range].

Team & context

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What decisions were made in Q4 planning?

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List all notes from meetings with [person].

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What meetings happened yesterday?

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Find meetings about the [project name] launch.

SEE HOW AUTH WORKS

Users authorize Granola once. Their workspace credentials stay vaulted, every call is checked, and every action is logged.

Authorize

Your user connects

Granola

once. We tie it to their identity and the meetings they approved — no shared bot account, no org-wide access

Who:

user ‘A’

when:

Once per user

access:

A’s meetings only

Store

Their

Granola

token lives in a vault scoped to them. User A's meetings are never reachable by an agent acting for user B, even on the same connection

vault:

encrypted

scope:

per-user

tokens:

auto-refreshed

Resolve

When your agent calls a

Granola

tool, we fetch the right token server-side. It never touches your agent, never appears in the LLM context, never shows up in your logs

speed:

~40ms

check:

before every call

seen by:

nobody

Audit

Every

Granola

tool call is logged — who triggered it, which meeting was fetched, what came back. 90 days of history, tied to the user who authorized it

history:

90 days

export:

SIEM-ready

logged:

every call

Test other agents

See the same per-user auth pattern across other productivity and meeting connectors.

SALES

Sales call prep agent

Pull meeting notes from Granola, cross-reference contact history in Attio, and draft a pre-call brief before every sales meeting. Zero rep input.

Granola

Attio

View agent

SALES

Deal intelligence agent

Combine Gong call recordings, Attio contact data, and Slack signals to surface deal risks and next-best actions. Updated after every call.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

01.

Shared tokens break per-user analytics

A shared token looks fine in a demo. In production every call looks like a service account. Scalekit resolves the real user credential so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Granola today. Others tomorrow.

Capability

DIY

Scalekit AgentKit

Token storage

Build + maintain yourself

AES-256 vault, managed

Per-user isolation

Custom credential map

Per-tenant namespace, default

Scope enforcement

Manual checks or none

Per-request, pre-API call

Token refresh

Cron job you maintain

Automatic

Audit trail

Build your own logging

90-day, SIEM-exportable

New connector

New OAuth implementation

Same pattern, one config

Multi-framework

Per-framework adapter code

8 adapters included

“Our agents act across Salesforce, Gong, Google Drive, and more, on behalf of every customer. Scalekit behind the scenes meant we can keep adding tools without ever rebuilding how credentials or tool calling work.”

Venu Madhav Kattagoni

Head of Engineering / Von

On this page

Tool and actions Framework Compatibility Starter Prompt Auth Lifecycle Try Other Agents Why Scalekit

Start in your coding agent

Up and running in one command

Install the Scalekit skill in your editor of choice. Connector, auth, tools, prompt, all wired up

Claude Code REPL

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/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack

Cursor Code REPL

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# ~/.cursor/mcp.json
{
""mcpServers"": {
""granola"": {
""url"": ""https://mcp.scalekit.com/granola"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}

Codex Code REPL

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# ~/.codex/config.toml
[mcp_servers.granola]
url = ""https://mcp.scalekit.com/granola""
auth_env = ""SCALEKIT_TOKEN""

Copilot Code REPL

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# .vscode/mcp.json
{
""servers"": {
""granola"": {
""url"": ""https://mcp.scalekit.com/granola"",
""type"": ""http""
}
}
}