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 LangChain 8 tools — connect in under 2 minutes

Name: Zenkit
Availability: InStock
Author: Vinkius

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

Built by Vinkius GDPR 8 Tools Framework

LangChain is the leading Python framework for composable LLM applications. Connect Zenkit 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({
        "zenkit": {
            "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 Zenkit, show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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* 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.

LangChain's ecosystem of 500+ components combines seamlessly with Zenkit through native MCP adapters. Connect 8 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

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 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 Zenkit to LangChain via MCP

Follow these steps to integrate the Zenkit 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 8 tools from Zenkit via MCP

Why Use LangChain with the Zenkit MCP Server

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

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

Zenkit + LangChain Use Cases

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

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

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

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

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

Zenkit MCP Tools for LangChain (8)

These 8 tools become available when you connect Zenkit to LangChain 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 LangChain

Ready-to-use prompts you can give your LangChain 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 LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Zenkit + LangChain FAQ

Common questions about integrating Zenkit 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.