Stack AI MCP connector for AI agents

Stack AI MCP tools for AI agents

CALL ANY TOOL

10 tools covering create, delete, get.

stackaimcp_create_project

Create a new stack ai project from a natural-language descri

Create a new stack ai project from a natural-language description by generating its nodes and edges with ai assistance.

Parameters

Name

Type

Required

Description

description

string

Required

High-level description of what the project should do.

stackaimcp_edit_project

Edit an existing stack ai project using a natural-language d

stackaimcp_get_project

Retrieve a project's node and edge graph as a paginated, sel

stackaimcp_get_project_corrections

Re-validate a project draft and return paginated correction

stackaimcp_get_run

Fetch the per-node execution trace for a project run, filter

stackaimcp_list_connections

List the oauth and api-key connections the authenticated use

stackaimcp_list_knowledge_bases

List knowledge bases available to the authenticated user, wi

stackaimcp_list_projects

Fetch a paginated list of projects accessible to the authent

stackaimcp_list_providers_actions

List available stack ai integration providers and their acti

stackaimcp_list_triggers

List the cron, polling, and webhook triggers configured on a

stackaimcp_run_project

Execute a published stack ai project by supplying a key-valu

stackaimcp_search_kb

Search a stack ai knowledge base and return the top matching

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="stackaimcp")

mcp = MultiServerMCPClient({
    "stackaimcp": {
        "url": "https://mcp.scalekit.com/stackaimcp",
        "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: "stackaimcp" });

const openai = new OpenAI();
// Connect to MCP at https://mcp.scalekit.com/stackaimcp
// 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: "stackaimcp" });

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

Try these prompts

Paste any prompt into your agent to get started.

Get started

Copy the prompt

Copied

Return the authenticated user’s profile, active organization, plan, and paginated list of all organizations?

Copy the prompt

Copied

Run pre-flight validation checks on a project draft and return paginated errors and warnings with stable codes and fix h?

Advanced

Copy the prompt

Copied

Set the active organization for the current session, routing all subsequent org-scoped tools to that org?

Copy the prompt

Copied

Search a Stack AI knowledge base and return the top matching chunks ranked by relevance?

SEE HOW AUTH WORKS

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

Authorize

Your user connects

Stack 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

Store

Their

Stack 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

Resolve

When your agent calls a

Stack 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

Audit

Every

Stack 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.

No items found.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought.

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 Stack 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 Stack 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 Stack 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 Stack AI.

What happens when a user revokes Stack 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.

Can the agent edit Stack AI projects it did not create?
Only projects the authorizing user can edit in their Stack AI workspace. Project builds, runs, and trace inspection inherit workspace roles per call.

On this page

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