Can my AI automatically aggregate all open issues inside our active sprint?

Yes. First, request the active iteration window by calling `list_cycles`. The agent will isolate today's active cycle bounds. Following that, it will invoke `list_work_items` directly constrained by that cycle object, outputting an exhaustive summary of untouched or blocking tickets.

How does the agent handle custom workspace slugs or self-hosted URLs?

The system dynamically accepts a `PLANE_BASE_URL` credential. If you are doing an on-premise installation running inside your homelab or company AWS cloud, you merely inject your private domain (e.g., `https://plane.internal.acme.com`). The workspace slug is seamlessly accepted as a contextual execution parameter during standard query prompts.

Can I request specific details of a long-term module (epic)?

You can instruct the agent to execute `list_modules`. It extracts the cross-functional epics spanning your workspace project. Your AI can read these top-level module architectures to contextually understand what the core functionality of the project repository aims to achieve.

Plane MCP Server for OpenAI Agents SDK 6 tools — connect in under 2 minutes

Name: Plane
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 6 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Plane through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

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 agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Plane Assistant",
            instructions=(
                "You help users interact with Plane. "
                "You have access to 6 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Plane"
        )
        print(result.final_output)

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

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 Plane MCP Server

Equip any AI agent with direct access to your Plane workspace. Whether you're using Plane Cloud or self-hosting, your AI assistant can seamlessly retrieve projects, analyze agile cycles, and parse the active issues pipeline without forcing you to click through kanban boards manually.

The OpenAI Agents SDK auto-discovers all 6 tools from Plane through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Plane, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Projects & Core Entities — Extract isolated project architectures spanning across your Workspace, pulling high-fidelity descriptions and status parameters.
Agile Cycles (Sprints) — Command the agent to list and structure your active development cycles, evaluating timelines and completion statuses.
Work Items (Issues) — Perform deep sweeps over tasks and explicit tickets inside boundary states, analyzing what your engineering team is actually building.
Modules & Taxonomy — Read epics (modules) and static categorization labels dictating your issue pipelines.

The Plane MCP Server exposes 6 tools through the Vinkius. Connect it to OpenAI Agents SDK 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 Plane to OpenAI Agents SDK via MCP

Follow these steps to integrate the Plane MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 6 tools from Plane

Why Use OpenAI Agents SDK with the Plane MCP Server

OpenAI Agents SDK provides unique advantages when paired with Plane through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Plane + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Plane MCP Server delivers measurable value.

Automated workflows: build agents that query Plane, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Plane, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Plane tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Plane to resolve tickets, look up records, and update statuses without human intervention

Plane MCP Tools for OpenAI Agents SDK (6)

These 6 tools become available when you connect Plane to OpenAI Agents SDK via MCP:

get_project

Get specific details of a Plane project

list_cycles

List cycles inside a Plane project

list_labels

List project labels in Plane

list_modules

List modules inside a Plane project

list_projects

List projects in a Plane workspace

list_work_items

List work items (issues) inside a Plane project

Example Prompts for Plane in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Plane immediately.

"List all cross-functional modules targeting project `project-x`."

"Are there any pending work items left in the current active active sprint?"

"Fetch the exact details of the issue designated ID `3841-A` on the board."

Troubleshooting Plane MCP Server with OpenAI Agents SDK

Common issues when connecting Plane to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Plane + OpenAI Agents SDK FAQ

Common questions about integrating Plane MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.