Gong connector for AI agents

Tools your Gong agent reaches for, scoped per user.

CALL ANY TOOL

List a rep's calls, fetch a single call, pull a transcript, push an Engage flow. Same toolkit, every framework, no auth plumbing to maintain.

gong_calls_list

List calls

List Gong calls with optional filters for date range, workspace, and call IDs. Returns metadata including title, duration, participants, and direction.

Parameters

Name

Type

Required

Description

from_date_time

string

Optional

ISO 8601 start filter

to_date_time

string

Optional

ISO 8601 end filter

workspace_id

string

Optional

Gong workspace ID

cursor

string

Optional

Pagination cursor for next page

gong_calls_get

Get calls (extensive)

gong_calls_transcript_get

Get call transcripts

gong_engage_tasks_list

List Engage tasks

gong_engage_flows_list

List Engage flows

gong_engage_prospects_assign

Assign prospects to flow

gong_engage_task_complete

Complete Engage task

gong_stats_interaction

Get interaction statistics

gong_scorecards_list

List scorecards

gong_coaching_get

Get coaching data

gong_users_list

List users

gong_trackers_list

List trackers

Build your Agent

Drop the toolkit in, point it at the rep, and your agent can read Gong calls, transcripts, and coaching data from the first run.

Copied

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);

// list gong tools scoped to a specific user
const { tools } = await sk.tools.listScopedTools("user_123", {
 filter: { connectionNames: ["gong"], toolNames: ["gong_calls_list", "gong_calls_get", "gong_transcript_get"] },
 pageSize: 100,
});

// wrap each Scalekit tool as a LangChain DynamicStructuredTool
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: ["gong"], toolNames: ["gong_calls_list", "gong_calls_get", "gong_transcript_get"] },
 pageSize: 100,
});

// shape tools as OpenAI function definitions
const llmTools = tools.map((t) => ({
 type: "function" as const,
 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: ["gong"], toolNames: ["gong_calls_list", "gong_calls_get", "gong_transcript_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-4-5", max_tokens: 1024,
 tools: llmTools,
 messages: [{ role: "user", content: prompt }],
});

Copied

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/gong",
 headers: { Authorization: `Bearer ${userScopedToken}` },
 }}),
}});

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

Try these prompts

These are the questions sales leaders ask Gong every week. Copy any prompt, paste into your agent, watch it route to the right tool with the rep's scope.

Action & follow-up

Copy the prompt

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What call tasks and follow-ups are assigned to me this week?

Copy the prompt

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What did we commit to Acme on our last call?

Copy the prompt

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What are the open questions from yesterday's call?

Copy the prompt

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Draft a follow-up email based on my last call with Stripe.

Search & recall

Copy the prompt

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Find every call where pricing was discussed.

Copy the prompt

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What did [person] say about the roadmap?

Copy the prompt

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Summarise everything discussed about [feature] across all recorded calls.

Copy the prompt

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List all my calls from last month.

Prep & intel

Copy the prompt

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What context do I need before my next call with this account?

Copy the prompt

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Who was on my last call with [company] and what was decided?

Copy the prompt

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Get the full transcript from my most recent call with [account].

SEE HOW AUTH WORKS

Each rep authorises Gong once. Every agent call after that uses their token, runs a scope check, and lands in an exportable audit trail.

Authorize

Your user connects

Gong

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

Gong

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

Gong

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

Gong

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-rep auth pattern across other revenue-intel and meeting tools. Working code, live demos, fork what fits.

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.

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

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other agent connector libraries treat auth as a demo afterthought. Scalekit starts with rep identity, scope enforcement, and audit so production deploys hold up.

01.

Bot calls break rep-level analytics

A shared Gong token looks fine in a demo. In production, every call retrieved looks like it came from a service account. Audit logs break. Per-rep scoping breaks. Scalekit resolves the credential of the actual user who triggered the agent, never a shared bot.

// shared bot token
 token = "sk_gong_shared_xxx"
 audit → bot_service_account
 rep_filter → broken
 

 // scalekit · per-user
 token = resolve(user_id)
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Gong today. Chorus, Salesforce, HubSpot 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": {
"gong": {
"url": "https://mcp.scalekit.com/gong",
"headers": { "Authorization": "Bearer $SCALEKIT_TOKEN" }
}
}
}

Codex Code REPL

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

Copilot Code REPL

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