IcePanel MCP

Live

OAUTH 2.0

DEVELOPER TOOLS

Developer Tools

Connect your IcePanel software architecture models to AI agents. Query and update your C4 model landscapes — systems, apps, components, connections, and...

  • 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.
IcePanel MCP
agent · Acme Q3
Run
Icepanel Createadr in IcePanel MCP
S
icepanelmcp_icepanel_createadr
85ms
IcePanel MCP agent
Create a new architecture decision record (adr) in the landscape..
Sources: IcePanel MCP
icepanelmcpmcp
1 tool call
18:29
Message Claude...

IcePanel MCP tools for AI agents

CALL ANY TOOL
12 tools covering icepanel.
icepanelmcp_icepanel_listadrs
Icepanel listadrs
List Architecture Decision Records (ADRs) in the landscape.
Parameters
Name
Type
Required
Description
name
string
Optional
Filter ADRs by name.
responseFormat
string
Optional
Controls the verbosity of the response. Use 'detailed' for full ADR information or 'concise' for a summary.
status
string
Optional
Filter ADRs by status. Valid values: accepted, draft, rejected.
versionId
string
Optional
The landscape version ID to list ADRs from. Omit to use the latest version.
icepanelmcp_icepanel_listtags
Icepanel listtags
icepanelmcp_icepanel_createadr
Icepanel createadr
icepanelmcp_icepanel_listflows
Icepanel listflows
icepanelmcp_icepanel_listteams
Icepanel listteams
icepanelmcp_icepanel_updateadr
Icepanel updateadr
icepanelmcp_icepanel_listdomains
Icepanel listdomains
icepanelmcp_icepanel_listdiagrams
Icepanel listdiagrams
icepanelmcp_icepanel_getadrdetails
Icepanel getadrdetails
icepanelmcp_icepanel_getflowdetails
Icepanel getflowdetails
icepanelmcp_icepanel_getteamdetails
Icepanel getteamdetails
icepanelmcp_icepanel_landscapesearch
Icepanel landscapesearch
icepanelmcp_icepanel_listconnections
Icepanel listconnections
icepanelmcp_icepanel_createconnection
Icepanel createconnection
icepanelmcp_icepanel_getdomaindetails
Icepanel getdomaindetails
icepanelmcp_icepanel_listmodelobjects
Icepanel listmodelobjects
icepanelmcp_icepanel_listtechnologies
Icepanel listtechnologies
icepanelmcp_icepanel_updateconnection
Icepanel updateconnection
icepanelmcp_icepanel_createmodelobject
Icepanel createmodelobject
icepanelmcp_icepanel_getdiagramdetails
Icepanel getdiagramdetails
icepanelmcp_icepanel_updatemodelobject
Icepanel updatemodelobject
icepanelmcp_icepanel_getconnectiondetails
Icepanel getconnectiondetails
icepanelmcp_icepanel_gettechnologydetails
Icepanel gettechnologydetails
icepanelmcp_icepanel_getmodelobjectdetails
Icepanel getmodelobjectdetails
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="icepanelmcp")

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

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

const anthropic = new Anthropic();
// Connect to MCP at https://mcp.scalekit.com/icepanelmcp
// 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="icepanelmcp")
# Connect to MCP at https://mcp.scalekit.com/icepanelmcp
# Pass: Authorization: Bearer + token
Try these prompts
Paste any prompt into your agent to get started.
Get started
Copy the prompt
Copied
List technologies from the catalog and organization?
Copy the prompt
Copied
List connections where a model object is the origin or target?
Advanced
Copy the prompt
Copied
Search across all landscape entities (model objects, connections, diagrams, flows) by name?
Copy the prompt
Copied
Update a connection in the landscape?
SEE HOW AUTH WORKS
User authorises once. Every agent call after uses their token with scope enforcement.
1
Authorize
Your user connects
IcePanel 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
IcePanel 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
IcePanel 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
IcePanel 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.
Engineering Teams
DevOps assistant agent
Polls GitHub for failing checks and stale PRs, opens Linear issues for the ones that need work, and posts a daily digest to Slack. It acts as the engineer, not a shared service account.
Engineering Teams
Engineering standup agent
Pulls commits from GitHub and GitLab, tracks issue movement in Jira, and posts a per-engineer standup brief to Slack. Each engineer's activity is read on their own delegated OAuth.
Engineering Teams
Auto release notes agent
Reads merged GitHub PRs, groups them into structured release notes, publishes the page to Notion, and announces the release in Slack. Every call runs on the engineer's own delegated OAuth.
Engineering Teams
Slack triage
Polls Slack for new messages, classifies bugs and support requests with a LangGraph router, files GitHub issues or Zendesk tickets, and confirms in the thread.
Test other agents
See the same per-user auth pattern across other connectors.
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.
ENGINEERING
Auto-release notes agent
Group merged GitHub PRs into structured release notes, publish the page to Notion, and announce the release in Slack.
ENGINEERING
Engineering standup agent
Pull commits from GitHub and GitLab, track Jira issue movement, and post a per-engineer standup brief to Slack.
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.
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 IcePanel 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 IcePanel 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 IcePanel?
Yes. Pass a tool name filter to listScopedTools so the DevOps agent only sees the subset you authorize. Pre-API-call scope checks block out-of-policy actions before the request reaches IcePanel.

What happens when a user revokes IcePanel 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 modify our C4 architecture model?
Only landscapes the authorizing user can edit in IcePanel. Model objects, connections, and ADRs created by the agent attribute to that user, keeping the architecture change history honest.

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"": {
""icepanelmcp"": {
""url"": ""https://mcp.scalekit.com/icepanelmcp"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}
Codex Code REPL
# ~/.codex/config.toml
[mcp_servers.icepanelmcp]
url = ""https://mcp.scalekit.com/icepanelmcp""
auth_env = ""SCALEKIT_TOKEN""
Copilot Code REPL
# .vscode/mcp.json
{
""servers"": {
""icepanelmcp"": {
""url"": ""https://mcp.scalekit.com/icepanelmcp"",
""type"": ""http""
}
}
}