Plane MCP connector for AI agents

CALL ANY TOOL

Manage issues, cycles, modules, and epics in Plane. Same toolkit, every framework, no auth plumbing.

plane_issues_list

List issues

List work items in a Plane project with optional filters for assignee, priority, state, and cycle.

Parameters

Name

Type

Required

Description

project_id

string

Required

Plane project ID

assignee

string

Optional

Filter by assignee user ID or 'me'

priority

string

Optional

Filter by priority: urgent, high, medium, low, none

state

string

Optional

Filter by state: backlog, todo, in_progress, done, cancelled

cycle_id

string

Optional

Filter by sprint/cycle ID

plane_issue_create

Create issue

plane_issue_update

Update issue

plane_cycles_list

List cycles

plane_modules_list

List modules

Build your Agent

Drop the toolkit in, point it at the user, and your agent can manage Plane projects and work items from the first run.

import { ScalekitClient } from "@scalekit-sdk/node";
import { DynamicStructuredTool } from "@langchain/core/tools";
import { createReactAgent } from "@langchain/langgraph/prebuilt";
import { z } from "zod";

const sk = new ScalekitClient(envUrl, clientId, clientSecret);

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["planemcp"], toolNames: ["plane_issues_list", "plane_issue_create", "plane_issue_update"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: z.object({}).passthrough(),
  func: async (args) => {
    const { data } = await sk.tools.executeTool({
      toolName: t.tool.definition.name,
      identifier: "user_123",
      params: args,
    });
    return JSON.stringify(data);
  },
}));

const agent = createReactAgent({ llm, tools: lcTools });

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: ["planemcp"], toolNames: ["plane_issues_list", "plane_issue_create", "plane_issue_update"] },
  pageSize: 100,
});

const llmTools = tools.map((t) => ({
  type: "function",
  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: ["planemcp"], toolNames: ["plane_issues_list", "plane_issue_create", "plane_issue_update"] },
  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-4-6", max_tokens: 1024,
  tools: llmTools,
  messages: [{ role: "user", content: prompt }],
});

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

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

Try these prompts

Paste any prompt into your planning agent to start managing Plane work items and sprints.

Search & recall

Copy the prompt

Copied

List all high-priority issues assigned to me in the current sprint.

Copy the prompt

Copied

Show all backlog items with no assignee in [project].

Copy the prompt

Copied

List all cycles in [project] with completion percentage.

Action & create

Copy the prompt

Copied

Create a new issue: [title] with priority [high] assigned to [user].

Copy the prompt

Copied

Move all completed issues from the current cycle to done.

Copy the prompt

Copied

List all modules in [project] and their issue counts.

SEE HOW AUTH WORKS

Users authorize Plane once. Their workspace credentials stay vaulted, every issue action runs under their identity, and every call is logged.

1

Authorize

Your user connects

Plane 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

Plane 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

Plane 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

Plane 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

Same per-user auth pattern across other project management and productivity connectors.

No items found.

Why Scalekit

Secure your agent's access. Connectors ship in minutes

Other connector libraries treat auth as a demo afterthought. Scalekit starts with user identity, scope enforcement, and audit.

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 so attribution, audit, and scope stay accurate.

// shared token
 audit → bot_service_account
 user_filter → broken

 // scalekit
 audit → user_abc
 scope → enforced ✓

02.

Authentication is not authorization

03.

Multi-tenancy is architectural

04.

Plane MCP today. Others 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 Plane as the user or a shared account?

As the user. Each workspace member authorizes once and issues created by the agent appear under that user's Plane identity.

Where is the Plane OAuth token stored?

In Scalekit's AES-256 vault, namespaced per tenant. Tokens never appear in prompts or LLM context.

Can I restrict the agent to read-only access?

Yes. Use listScopedTools to allow issue listing and cycle queries without granting issue creation or state updates.

What happens when a user revokes Plane access?

The connection is invalidated on the next tool call. Subsequent requests fail closed. Other workspace members remain unaffected.

Can the agent create Plane issues from GitHub PRs in one workflow?

Yes. A single agent can pull GitHub PR data and create Plane issues in the same workflow, using the same user identity for both.

Plane MCP

Tools your agent reaches for on Plane MCP, scoped per user.