Plane MCP connector for AI agents

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

CALL ANY TOOL

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

planemcp_add_work_items_to_cycle

Add work items to cycle

Add one or more work items to a cycle by their UUIDs.

Parameters

Name

Type

Required

Description

cycle_id

string

Required

UUID of the cycle. Get it from List Cycles.

issue_ids

array

Required

List of work item UUIDs to add.

project_id

string

Required

UUID of the project. Get it from List Projects.

planemcp_archive_cycle

Archive cycle

planemcp_create_cycle

Create cycle

planemcp_create_intake_work_item

Create intake work item

planemcp_create_project

Create project

planemcp_create_work_item

Create work item

planemcp_create_work_item_relation

Create work item relation

planemcp_delete_cycle

Delete cycle

planemcp_delete_intake_work_item

Delete intake work item

planemcp_delete_project

Delete project

planemcp_delete_work_item_link

Delete work item link

planemcp_get_me

Get me

planemcp_get_project_worklog_summary

Get project worklog summary

planemcp_list_archived_cycles

List archived cycles

planemcp_list_epics

List epics

planemcp_list_milestone_work_items

List milestone work items

planemcp_list_states

List states

planemcp_list_work_item_properties

List work item properties

planemcp_remove_work_item_from_cycle

Remove work item from cycle

planemcp_retrieve_cycle

Retrieve cycle

planemcp_retrieve_label

Retrieve label

planemcp_retrieve_project_page

Retrieve project page

planemcp_retrieve_work_item_by_identifier

Retrieve work item by identifier

planemcp_retrieve_work_item_type

Retrieve work item type

planemcp_search_work_items

Search work items

planemcp_update_cycle

Update cycle

planemcp_update_intake_work_item

Update intake work item

planemcp_update_module

Update module

planemcp_update_work_item

Update work item

planemcp_update_work_item_property

Update work item property

For more tools, view docs.

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.

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

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

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

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.

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

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

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.

People Ops and HR teams

Performance review collector

Collects review feedback from Airtable and Google Forms scoped to each manager's direct reports, writes per-employee summaries to Notion, and DMs the manager a Slack digest.

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 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.

On this page

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