Read AI MCP connector for AI agents

CALL ANY TOOL

3 tools covering get, create, list.

readaimcp_list_meetings

List meetings

List Read AI meetings for the authenticated user with optional start-time filters and cursor-based pagination. Returns up to 10 meetings per page. When to use: Use this tool to browse, search, or paginate through meetings : for example, to find all meetings within a date range, retrieve recent meetings, or iterate through all meetings using cursor-based pagination. When NOT to use: Do not use this tool when you already know the meeting ULID and need full content : use get_meeting_by_id instead.

Parameters

Name

Type

Required

Description

cursor

string

Optional

Cursor for pagination. Pass the ULID of the last meeting from the previous page to retrieve the next page.

expand

string

Optional

List of expandable fields to include in the response for each meeting. When omitted, only base metadata is returned.

limit

integer

Optional

The number of results to return per page. Maximum is 10.

start_datetime_gt

string

Optional

Only return meetings with start times strictly after this datetime (exclusive lower bound). ISO 8601 format.

start_datetime_gte

string

Optional

Only return meetings with start times on or after this datetime (inclusive lower bound). ISO 8601 format.

start_datetime_lt

string

Optional

Only return meetings with start times strictly before this datetime (exclusive upper bound). ISO 8601 format.

start_datetime_lte

string

Optional

Only return meetings with start times on or before this datetime (inclusive upper bound). ISO 8601 format.

readaimcp_get_meeting_by_id

Get meeting by id

readaimcp_create_meeting_agent

Create meeting agent

readaimcp_share_meeting_report

Share meeting report

Build your Agent

Same auth pattern across every framework.

from langchain_mcp_adapters.client import MultiServerMCPClient
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="readaimcp")

mcp = MultiServerMCPClient({
    "readaimcp": {
        "url": "https://mcp.scalekit.com/readaimcp",
        "headers": {"Authorization": "Bearer " + token}
    }
})
tools = await mcp.get_tools()

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

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "readaimcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/readaimcp
// Pass: Authorization: Bearer + token

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

const client = new ScalekitClient({ envUrl, clientId, clientSecret });
const token = await client.agent.getToken({ userId: "user_id", connector: "readaimcp" });

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/readaimcp
// Pass: Authorization: Bearer + token

from google.adk.agents import LlmAgent
from scalekit import ScalekitClient

client = ScalekitClient(env_url=ENV_URL, client_id=CLIENT_ID, client_secret=SECRET)
token = client.agent.get_token(user_id="user_id", connector="readaimcp")
# Connect to MCP at https://mcp.scalekit.com/readaimcp
# Pass: Authorization: Bearer + token

Try these prompts

Paste any prompt into your agent to get started.

Get started

Copy the prompt

Copied

List Read AI meetings for the authenticated user with optional start-time filters and cursor-based pagination?

Copy the prompt

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Retrieve a single Read AI meeting by its ULID identifier, with optional expansion of rich meeting content such as summar?

Advanced

Copy the prompt

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Send a Read AI meeting agent (bot) to a video conferencing meeting to record and transcribe it?

SEE HOW AUTH WORKS

User authorises once. Every agent call after uses their token with scope enforcement.

1

Authorize

Your user connects

Read AI MCP

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:

Limited to user

2

Store

Their

Read AI MCP

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

3

Resolve

When your agent calls a

Read AI MCP

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

4

Audit

Every

Read AI MCP

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 connectors.

GTM and RevOps Teams

CRM AI agent

Reads the Granola transcript after every call, extracts next steps and updates the HubSpot record, drafts the follow-up in Gmail, and confirms in Slack, all on the rep's own delegated OAuth.

GTM and RevOps Teams

Sales call prep agent

Reads tomorrow's calls from Google Calendar, mines past Granola notes and Attio history for context, and delivers each rep a prep brief in Slack, scoped to the calls that rep actually owns.

GTM and RevOps Teams

Deal intelligence agent

Pulls recent Gong calls, scores deal risk with an LLM, cross-references the record in Attio, and DMs each owner their at-risk deals in Slack. Every read is scoped to that rep's own access.

GTM and RevOps Teams

Competitive intelligence briefing agent

Scans Gong calls for competitor mentions, matches each one to its Notion battlecard, and DMs every affected rep a single Slack digest per cycle. Every call runs as the PMM who owns the briefing, never a shared bot.

Test other agents

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

SALES

Deal intelligence agent

Score Gong call risk with an LLM, cross-reference the Attio record, and DM each owner their at-risk deals in Slack.

GTM

CRM AI agent

Turn each Granola call transcript into a HubSpot record update, a drafted Gmail follow-up, and a Slack recap.

SALES

Sales call prep agent

Read tomorrow's calls from Google Calendar, mine Granola notes and Attio history, and deliver each rep a prep brief in Slack.

GTM

Competitive intelligence briefing agent

Scan Gong calls for competitor mentions, match each one to its Notion battlecard, and DM every affected rep a single Slack digest.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

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.

// shared token
 audit → bot_service_account

 // scalekit
 audit → user_abc ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

One connector today. Ten tomorrow.

“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

FAQs

Frequently Asked Questions

Does the agent access Read AI as the user or as a shared key?
As the user. Each workspace member authorizes once and Scalekit resolves their credential at request time. Audit logs attribute every action to that user, not a shared service account.

Where is the Read AI OAuth token stored?
In Scalekit's managed AES-256 token vault, namespaced per tenant. Refresh is automatic. Revocation is a single dashboard action. Tokens never appear in prompts, logs, or LLM context.

Can I limit what the agent is allowed to do in Read AI?
Yes. Pass a tool name filter to listScopedTools so the AI agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches Read AI.

What happens when a user revokes Read AI access?
The connection is invalidated on the next tool call. Subsequent requests for that user fail closed with a clear error. Other users in the tenant remain unaffected. The event is logged for audit.

Which meetings can the agent pull intelligence from?
Only meetings visible to the authorizing user in Read AI. Transcripts, summaries, and action items follow the user's meeting access, so other people's 1:1s stay private.

Read AI MCP

Read AI MCP tools for AI agents