How to Use the Linear MCP in LlamaIndex

Q: Can LlamaIndex use Linear to answer questions about my sprint?

Yes, by calling listcycles, your agent can retrieve sprint data and add it to your knowledge base. This allows for natural language queries about progress and completion rates.

Q: Does this support multi-team Linear setups in LlamaIndex?

It does. You can use listteams to discover IDs and then filter your index or search queries by specific team boundaries.

Q: What happens if Linear updates an issue in my LlamaIndex index?

Your agent can use getissue to refresh the record. You should trigger this update periodically to ensure your index reflects the most current issue state.

Build RAG apps in LlamaIndex that index your Linear backlog for semantic search and grounded answers.

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Connect Linear MCP to LlamaIndex

Create your Vinkius account to connect Linear to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Linear with LlamaIndex

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Claude

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Index your Linear backlog in LlamaIndex

Turn your issue history into a searchable knowledge base. Use `list_issues` to fetch your backlog and index the content directly into your vector store. Your RAG application gains the ability to find past decisions buried in old tickets. This makes your agent significantly more accurate when answering technical questions.

Query project data with LlamaIndex

Ask questions about your project status and get answers backed by live data. Your agent uses `get_project` and `search_issues` to ground its responses in reality. This prevents the common hallucination issues you see with standard chatbots. The agent reads the actual issue state before providing an answer.

Live issue retrieval and updates

Keep your knowledge base fresh by pulling updates on demand. Use `get_issue` to pull the latest details for a specific ticket and add them to your index. When you need to act, use `create_issue` or `update_issue` to modify the state. It allows your LlamaIndex agents to be both observers and participants in your workflow.

Setup guide

Set up Linear MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Linear MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Linear tools.",
)
response = await agent.run("List recent Linear data")

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Common questions about Linear MCP in LlamaIndex

The MCP Server provides the tools to pull issue data, which you then feed into your LlamaIndex vector store. Once indexed, you can perform semantic searches across your entire backlog.

Yes, by calling `list_cycles`, your agent can retrieve sprint data and add it to your knowledge base. This allows for natural language queries about progress and completion rates.

Data privacy is handled at the transport layer. The MCP Server acts as a secure pipe, ensuring that your issue content and comments are only accessible by your authenticated agent.

It does. You can use `list_teams` to discover IDs and then filter your index or search queries by specific team boundaries.

Your agent can use `get_issue` to refresh the record. You should trigger this update periodically to ensure your index reflects the most current issue state.

Use it with your favorite AI tools

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