Stack AI MCP

Live

OAUTH 2.0

AI

AI

Connect to Stack AI MCP. Deploy and manage enterprise AI workflows, automate data pipelines, and run AI models securely from your AI agent workflows.

  • Acts as the user: Every tool call runs as the authorizing user. Access and audit trail stay intact.
  • Credentials stay vaulted: AES-256 encrypted, resolved at request time, never stored in LLM context.
  • Scoped before every call: Per-user permissions enforced automatically. 90-day audit trail included.
Stack AI MCP
agent · Acme Q3
Run
Create Project in Stack.ai MCP
S
stackaimcp_create_project
85ms
Stack.ai MCP agent
Create a new stack ai project from a natural-language description by generating its nodes and edges with ai assistance..
Sources: Stack.ai MCP
stackaimcpmcp
1 tool call
18:29
Message Claude...

Stack AI MCP tools for AI agents

CALL ANY TOOL
10 tools covering create, delete, get.
stackaimcp_create_project
Create project
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 project
stackaimcp_get_project
Get project
stackaimcp_get_project_corrections
Get project corrections
stackaimcp_get_run
Get run
stackaimcp_list_connections
List connections
stackaimcp_list_knowledge_bases
List knowledge bases
stackaimcp_list_projects
List projects
stackaimcp_list_providers_actions
List providers actions
stackaimcp_list_triggers
List triggers
stackaimcp_run_project
Run project
stackaimcp_search_kb
Search kb
stackaimcp_switch_org
Switch org
stackaimcp_validate_workflow
Validate workflow
stackaimcp_whoami
Whoami
Build your Agent
Same auth pattern across every framework.
Python · LlamaIndex
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.
1
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
2
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
3
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
4
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.
OPS
Email-to-calendar scheduling agent
Read scheduling intent out of Gmail threads, resolve mutual free time, and create the Google Calendar event.
ENGINEERING
Slack triage agent
Classify new Slack messages as bugs or support requests, file the GitHub issue or Zendesk ticket, and reply in the thread.
SUPPORT
Support ticket automation (Google ADK)
Fetch, annotate, and archive Zendesk tickets with Notion context, digesting anything it cannot answer to Slack.
ENGINEERING
DevOps assistant agent
Poll GitHub for failing checks and stale pull requests, open Linear issues for the ones that need work, and digest to Slack.
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 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.

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
/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack
Cursor Code REPL
# ~/.cursor/mcp.json
{
""mcpServers"": {
""stackaimcp"": {
""url"": ""https://mcp.scalekit.com/stackaimcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.stackaimcp]
url = ""https://mcp.scalekit.com/stackaimcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""stackaimcp"": {
""url"": ""https://mcp.scalekit.com/stackaimcp"",
""type"": ""http""
}
}
}