Linear MCP Server for LangChain 12 tools — connect in under 2 minutes
LangChain is the leading Python framework for composable LLM applications. Connect Linear through the Vinkius and LangChain agents can call every tool natively — combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from langchain_mcp_adapters.client import MultiServerMCPClient
from langchain_openai import ChatOpenAI
from langgraph.prebuilt import create_react_agent
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
async with MultiServerMCPClient({
"linear": {
"transport": "streamable_http",
"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
}
}) as client:
tools = client.get_tools()
agent = create_react_agent(
ChatOpenAI(model="gpt-4o"),
tools,
)
response = await agent.ainvoke({
"messages": [{
"role": "user",
"content": "Using Linear, show me what tools are available.",
}]
})
print(response["messages"][-1].content)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Linear MCP Server
Connect your Linear workspace to any AI agent and take full control of your issue tracking and sprint workflows through natural conversation.
LangChain's ecosystem of 500+ components combines seamlessly with Linear through native MCP adapters. Connect 12 tools via the Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures — with LangSmith tracing giving full visibility into every tool call, latency, and token cost.
What you can do
- User & Team Discovery — Retrieve the authenticated user profile and list all teams configured in your Linear workspace
- Issue Management — List, search, inspect and create issues with full metadata including assignees, labels, priority and state
- Project Oversight — Browse all active projects, view their status and drill into specific project details by ID
- Comments & Collaboration — Add comments to issues to keep your team context aligned without switching to the Linear app
- Cycle Tracking — List all sprint cycles for any team, including start/end dates and completion progress
- Label Organization — View all issue labels used for categorization across teams
The Linear MCP Server exposes 12 tools through the Vinkius. Connect it to LangChain in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect Linear to LangChain via MCP
Follow these steps to integrate the Linear MCP Server with LangChain.
Install dependencies
Run pip install langchain langchain-mcp-adapters langgraph langchain-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save the code and run python agent.py
Explore tools
The agent discovers 12 tools from Linear via MCP
Why Use LangChain with the Linear MCP Server
LangChain provides unique advantages when paired with Linear through the Model Context Protocol.
The largest ecosystem of integrations, chains, and agents — combine Linear MCP tools with 500+ LangChain components
Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step
LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging
Memory and conversation persistence let agents maintain context across Linear queries for multi-turn workflows
Linear + LangChain Use Cases
Practical scenarios where LangChain combined with the Linear MCP Server delivers measurable value.
RAG with live data: combine Linear tool results with vector store retrievals for answers grounded in both real-time and historical data
Autonomous research agents: LangChain agents query Linear, synthesize findings, and generate comprehensive research reports
Multi-tool orchestration: chain Linear tools with web scrapers, databases, and calculators in a single agent run
Production monitoring: use LangSmith to trace every Linear tool call, measure latency, and optimize your agent's performance
Linear MCP Tools for LangChain (12)
These 12 tools become available when you connect Linear to LangChain via MCP:
create_comment
The body supports Linear Markdown format including @mentions and ~~strikethrough~~. Add a comment to a Linear issue
create_issue
Requires the team ID and issue title. Optionally set description, assignee, priority (0=No priority, 1=Urgent, 2=High, 3=Normal, 4=Low) and label IDs. Create a new Linear issue
get_issue
Use the issue ID (UUID) or the human-readable identifier (e.g. TEAM-123). Get full details for a Linear issue
get_project
Get details for a specific Linear project
get_viewer
Useful to verify which account the API token belongs to. Get current authenticated Linear user details
list_cycles
Each cycle has a number, name, start date, end date and completion progress percentage. List Linear cycles (sprints) for a team
list_issues
Optionally filter by team ID to get issues for a specific team only. List Linear issues
list_labels
Optionally filter by team ID. Each label has a name, color and optional description. List Linear issue labels
list_projects
Projects group issues across multiple teams. Use optional limit to control how many results to fetch. List Linear projects
list_teams
Each team has a unique ID, name, key prefix and optional description. Use this to discover teams before querying their issues or cycles. List all Linear teams
search_issues
Optionally filter results to a specific team. Returns issues with identifier, title, state, priority, assignee and URL. Search Linear issues by text
update_issue
Provide the issue ID (UUID) and only the fields you want to change. Update an existing Linear issue
Example Prompts for Linear in LangChain
Ready-to-use prompts you can give your LangChain agent to start working with Linear immediately.
"Show me all unresolved issues assigned to the Engineering team."
"Create a new issue in the Backend team titled 'Add rate limiting to /api/search endpoint' with high priority."
"What's the current sprint cycle progress for the Mobile team?"
Troubleshooting Linear MCP Server with LangChain
Common issues when connecting Linear to LangChain through the Vinkius, and how to resolve them.
MultiServerMCPClient not found
pip install langchain-mcp-adaptersLinear + LangChain FAQ
Common questions about integrating Linear MCP Server with LangChain.
How does LangChain connect to MCP servers?
langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.Which LangChain agent types work with MCP?
Can I trace MCP tool calls in LangSmith?
Connect Linear with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Linear to LangChain
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.