How do I query cycle time for a specific team?

Use the `query_software_metrics` tool and include the team name or ID in the `group_by` parameter of your JSON query.

What is the difference between coding_time and pickup_time?

Coding time is the duration from the first commit to the PR creation. Pickup time is the duration from the PR creation to the first review activity.

Can I report a release from the agent?

Absolutely. Use the `record_new_deployment` tool with the Git SHA or tag and the repository ID to inform LinearB that a deployment has occurred.

LinearB MCP Server for LangChain 7 tools — connect in under 2 minutes

Name: LinearB
Availability: InStock
Author: Vinkius

python.langchain.com/docs/integrations/tools/mcp

Built by Vinkius GDPR 7 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect LinearB through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

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({
        "linearb": {
            "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 LinearB, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

Fully ManagedVinkius Servers

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DLPData protection

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Ed25519Audit chain

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 LinearB MCP Server

Connect your LinearB account to any AI agent to automate your engineering intelligence and DORA metrics reporting. This MCP server enables your agent to query cycle time, track deployments, and report incidents directly from natural language interfaces.

LangChain's ecosystem of 500+ components combines seamlessly with LinearB through native MCP adapters. Connect 7 tools via 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

Metric Ingestion — Query complex engineering metrics including cycle time, coding time, and pickup time across teams
Deployment Management — Inform LinearB of new software releases by reporting Git refs (SHAs or tags) programmatically
Incident Tracking — Report and list engineering incidents to maintain accurate Change Failure Rate and MTTR metrics
Metadata Oversight — List teams and connected repositories to map technical IDs to organizational structures
DORA Analytics — Retrieve aggregated performance data to identify bottlenecks in your delivery pipeline

The LinearB MCP Server exposes 7 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 LinearB to LangChain via MCP

Follow these steps to integrate the LinearB 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 7 tools from LinearB via MCP

Why Use LangChain with the LinearB MCP Server

LangChain provides unique advantages when paired with LinearB through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine LinearB 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 LinearB queries for multi-turn workflows

LinearB + LangChain Use Cases

Practical scenarios where LangChain combined with the LinearB MCP Server delivers measurable value.

RAG with live data: combine LinearB tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query LinearB, synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain LinearB tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every LinearB tool call, measure latency, and optimize your agent's performance

LinearB MCP Tools for LangChain (7)

These 7 tools become available when you connect LinearB to LangChain via MCP:

list_connected_repos

List all connected repositories

list_engineering_teams

List all teams defined in LinearB

list_software_deployments

List recent deployments

list_software_incidents

List engineering incidents

query_software_metrics

Requires a JSON body with requested_metrics and time_ranges. Query software engineering metrics (v2)

record_new_deployment

Requires repo_id and ref. Report a new deployment to LinearB

record_new_incident

Requires provider_id and started_at. Report a new incident

Example Prompts for LinearB in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with LinearB immediately.

"Query the average cycle_time for the last 30 days for team 'Backend'."

"Record a new deployment for repo ID '123' with Git ref 'v1.2.0'."

"Report a new incident starting now for provider 'OpsGenie'."

Troubleshooting LinearB MCP Server with LangChain

Common issues when connecting LinearB to LangChain through the Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

LinearB + LangChain FAQ

Common questions about integrating LinearB MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use 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?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.