Linear MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Linear as an MCP tool provider through the Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token — get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to Linear. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in Linear?"
)
print(response)
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.
LlamaIndex agents combine Linear tool responses with indexed documents for comprehensive, grounded answers. Connect 12 tools through the Vinkius and query live data alongside vector stores and SQL databases in a single turn — ideal for hybrid search, data enrichment, and analytical workflows.
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 LlamaIndex 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 LlamaIndex via MCP
Follow these steps to integrate the Linear MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 12 tools from Linear
Why Use LlamaIndex with the Linear MCP Server
LlamaIndex provides unique advantages when paired with Linear through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Linear tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Linear tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Linear, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Linear tools were called, what data was returned, and how it influenced the final answer
Linear + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Linear MCP Server delivers measurable value.
Hybrid search: combine Linear real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Linear to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying Linear for fresh data
Analytical workflows: chain Linear queries with LlamaIndex's data connectors to build multi-source analytical reports
Linear MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect Linear to LlamaIndex 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 LlamaIndex
Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex
Common issues when connecting Linear to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpLinear + LlamaIndex FAQ
Common questions about integrating Linear MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
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 LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.