How do I find my List ID?

Use the `list_workspaces` tool to see all your workspaces and the lists they contain along with their unique IDs.

Can I filter entries in a list?

The current `list_entries` tool retrieves all items. For complex filtering, you can provide field-specific values when using the create or update tools.

Is it possible to see the field definitions of a list?

Absolutely. Use the `list_elements` tool with a list ID to retrieve all field names, types, and configurations.

Zenkit MCP Server for OpenAI Agents SDK 8 tools — connect in under 2 minutes

Name: Zenkit
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 8 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Zenkit 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="Zenkit Assistant",
            instructions=(
                "You help users interact with Zenkit. "
                "You have access to 8 tools."
            ),
            mcp_servers=[mcp_server],
        )

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

asyncio.run(main())

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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 Zenkit MCP Server

Connect your Zenkit account to any AI agent to streamline your productivity and project management. This MCP server enables your agent to interact with workspaces, lists (collections), and data entries directly from natural language.

The OpenAI Agents SDK auto-discovers all 8 tools from Zenkit through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Zenkit, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Workspace Oversight — List all workspaces and retrieve their constituent lists and metadata
List Management — Query detailed configurations and field elements for any Zenkit list
Data Operations — List, retrieve, create, and update entries (items) within your collections
Field Discovery — Inspect list elements to understand the data structure and field types
Content Cleanup — Delete entries and maintain your lists directly via natural language commands

The Zenkit MCP Server exposes 8 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 Zenkit to OpenAI Agents SDK via MCP

Follow these steps to integrate the Zenkit 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 8 tools from Zenkit

Why Use OpenAI Agents SDK with the Zenkit MCP Server

OpenAI Agents SDK provides unique advantages when paired with Zenkit 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

Zenkit + OpenAI Agents SDK Use Cases

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

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

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

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

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

Zenkit MCP Tools for OpenAI Agents SDK (8)

These 8 tools become available when you connect Zenkit to OpenAI Agents SDK via MCP:

create_entry

Requires a JSON object with field values. Create a new entry in a list

delete_entry

Delete an entry from a list

get_list_details

Get details for a specific list

get_workspace_details

Get details for a specific workspace

list_elements

List all elements (fields) defined in a list

list_entries

List all entries (items) in a list

list_workspaces

List all workspaces and their lists

update_entry

Update an existing entry

Example Prompts for Zenkit in OpenAI Agents SDK

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

"List all my Zenkit workspaces and their collections."

"Show me all entries in the list with ID '98765'."

"Create a new entry in list '98765' with name 'Finish API documentation'."

Troubleshooting Zenkit MCP Server with OpenAI Agents SDK

Common issues when connecting Zenkit 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.

Zenkit + OpenAI Agents SDK FAQ

Common questions about integrating Zenkit 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.