Linear connector for AI agents

Tools your Linear agent reaches for, scoped per user.

CALL ANY TOOL

Read issues, manage projects and cycles, update status, and search across the workspace. Same toolkit, every framework, no auth plumbing.

linear_issues_list

List issues

Fetch issues with optional filters for assignee, state, team, label, and cycle.

Parameters

Name

Type

Required

Description

team_id

string

Optional

Filter by team ID

assignee_id

string

Optional

Filter by assignee user ID

state

string

Optional

Filter by state name (e.g. Todo, In Progress, Done)

label

string

Optional

Filter by label name

first

integer

Optional

Max issues to return

linear_issue_get

Get issue

linear_issue_create

Create issue

linear_issue_update

Update issue

linear_search_issues

Search issues

linear_projects_list

List projects

Build your Agent

Drop the toolkit in, point it at the user, and your agent can list Linear issues, create tickets, and update status from the first run.

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

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

const { tools } = await sk.tools.listScopedTools("user_123", {
  filter: { connectionNames: ["linear"], toolNames: ["linear_issues_list", "linear_search_issues", "linear_issue_create"] },
  pageSize: 100,
});

const lcTools = tools.map((t) => new DynamicStructuredTool({
  name: t.tool.definition.name,
  description: t.tool.definition.description,
  schema: t.tool.definition.input_schema,
  func: async (args) => sk.tools.executeTool({
    toolName: t.tool.definition.name, identifier: "user_123", toolInput: args,
  }),
}));

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

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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: ["linear"], toolNames: ["linear_issues_list", "linear_search_issues", "linear_issue_create"] },
  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: ["linear"], toolNames: ["linear_issues_list", "linear_search_issues", "linear_issue_create"] },
  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 }],
});

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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/linear",
    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 agent to start managing Linear workflows.

Search & recall

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List all open issues assigned to me in [team].

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What issues are In Progress in the current cycle?

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Find all bugs labeled [critical] in [team].

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What issues are in [project name]?

Action & creation

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Create an issue in [team]: [title] — [description], priority High.

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Move issue [ENG-123] to In Progress.

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Assign [ENG-456] to [person name].

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Add label [backend] to issue [ENG-789].

Cycles & projects

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What is in the current cycle for [team]?

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List all issues not yet started in the current cycle.

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How many open issues are there by priority in [team]?

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What projects are active in [team] right now?

SEE HOW AUTH WORKS

Users authorize Linear once. Their workspace credentials stay vaulted, every call is checked, and every action is logged.

Authorize

Your user connects

Linear

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:

A’s meetings only

Store

Their

Linear

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

Linear

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

Linear

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 Jira, GitHub, and other project management connectors.

ENGINEERING

DevOps assistant agent

Triage GitHub incidents, open Linear tickets, and notify the on-call channel in Slack with context already attached.

ENGINEERING

Engineering standup agent

Aggregate GitHub and GitLab activity, link to Jira tickets, and post a daily standup digest to Slack. No async updates to chase.

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.

Linear today. Others tomorrow.

Capability

DIY

Scalekit AgentKit

Token storage

Build + maintain yourself

AES-256 vault, managed

Per-user isolation

Custom credential map

Per-tenant namespace, default

Scope enforcement

Manual checks or none

Per-request, pre-API call

Token refresh

Cron job you maintain

Automatic

Audit trail

Build your own logging

90-day, SIEM-exportable

New connector

New OAuth implementation

Same pattern, one config

Multi-framework

Per-framework adapter code

8 adapters included

“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

On this page

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

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

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/plugin marketplace add scalekit-inc/claude-code-authstack
/plugin install agentkit@scalekit-auth-stack

Cursor Code REPL

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# ~/.cursor/mcp.json
{
""mcpServers"": {
""linear"": {
""url"": ""https://mcp.scalekit.com/linear"",
""headers"": { ""Authorization"": ""Bearer $SCALEKIT_TOKEN"" }
}
}
}

Codex Code REPL

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# ~/.codex/config.toml
[mcp_servers.linear]
url = ""https://mcp.scalekit.com/linear""
auth_env = ""SCALEKIT_TOKEN""

Copilot Code REPL

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# .vscode/mcp.json
{
""servers"": {
""linear"": {
""url"": ""https://mcp.scalekit.com/linear"",
""type"": ""http""
}
}
}