How to Use the LinearB MCP in CrewAI

Q: Can my CrewAI agents report LinearB deployments?

Yes, by giving them the recordnewdeployment tool. The agent can trigger this after a successful automated test phase.

Deploy autonomous agent crews to monitor your software delivery performance using the LinearB MCP server.

See Vinkius in Action

Works with every AI agent you already use

…and any MCP-compatible client

MCP Servers - Free for Subscribers

Connect LinearB MCP to CrewAI

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

GDPR Free for Subscribers

Setup LinearB with CrewAI

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Autonomous metrics monitoring

Deploy a dedicated agent to run `query_software_metrics` on a schedule. Your crew can analyze delivery trends and flag bottlenecks before they affect the entire organization. The agents operate with shared memory. If one agent finds a dip in deployment frequency, it can alert the moderator agent to investigate specific repos via `list_connected_repos`.

Incident investigation crews

Assign an agent to monitor `list_software_incidents` while another agent prepares documentation. When a new incident appears, the crew automatically correlates it with recent changes from `list_software_deployments`. This removes the manual toil of cross-referencing logs. The agents do the heavy lifting, providing a summary of the incident and its potential root cause.

Team performance collaboration

Use `list_engineering_teams` to allow your agents to specialize their analysis. One agent can focus on high-performing teams, while another identifies teams that need additional support based on their current metrics. This hierarchical execution makes your operation more efficient. The crew understands which teams own which repositories, allowing for surgical interventions instead of broad, ineffective emails.

Setup guide

Set up LinearB MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke LinearB tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="LinearB Analyst",
    goal="Access and analyze LinearB data via MCP.",
    backstory="Expert analyst with direct LinearB access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent LinearB transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

Get your connection token →

Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="LinearB Analyst",
        goal="Access and analyze LinearB data via MCP.",
        backstory="Expert analyst with direct LinearB access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent LinearB transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

    crew = Crew(agents=[agent], tasks=[task])
    result = crew.kickoff()
    print(result)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by LinearB. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

Why Choose Vinkius

Vinkius connects your tools to AI with real-time monitoring and automatic cost savings — all from one dashboard.

Connect LinearB now

Real-time monitoring

Live

visibility into every interaction

Connect your favorite tools to your AI and see exactly what's happening — every request, every response, in real time.

Built-in savings

60%

lower AI costs

Vinkius compresses data between your apps and your AI automatically. Lower bills every month — no configuration required.

Single dashboard

One

place for every integration

Every tool your AI connects to, managed from a single screen. One account, complete control.

Common questions about LinearB MCP in CrewAI

Include the LinearB MCP server URL in the agent's MCP configuration. You can filter the tools to limit the agent's access to specific functions like `query_software_metrics`.

Yes, by giving them the `record_new_deployment` tool. The agent can trigger this after a successful automated test phase.

CrewAI uses role-based specialization. You assign the LinearB tools to the agent responsible for operational data, ensuring consistent data handling across the crew.

The MCP server is ephemeral and respects your endpoint tokens. Your operational records are never exposed beyond the specific agents that require the data.

It provides deployment logs, incident records, and repository metadata. This data is used strictly for operational analysis and is not stored externally.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

OpenAI Agents SDK sdk-python

Google ADK sdk-python

Pydantic AI sdk-python

Vercel AI SDK sdk-typescript