Zenodo MCP Server for LangChainGive LangChain instant access to 14 tools to Create Deposition, Delete Deposition, Delete Deposition File, and more

Name: Zenodo
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

Ask AI about this MCP Server for LangChain

Open in ChatGPT Open in Claude Open in Perplexity

The Zenodo MCP Server for LangChain is a standout in the Knowledge Management category — giving your AI agent 14 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

Claude

ChatGPT

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Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

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

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 Zenodo MCP Server

Connect your Zenodo account to any AI agent to streamline your scientific research workflows and data management through natural conversation.

LangChain's ecosystem of 500+ components combines seamlessly with Zenodo through native MCP adapters. Connect 14 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

Deposition Management — Create new unpublished depositions, update metadata, and manage your research drafts directly from the AI.
Record Discovery — Search and list public records across the entire Zenodo database to find relevant research, software, or datasets.
File Inspection — List all files attached to specific depositions to understand the contents of a research package.
Metadata Control — Precisely update titles, creators, descriptions, licenses, and access rights for your unpublished work.
Version Tracking — Retrieve specific deposition details using unique IDs to monitor the status of your submissions.

The Zenodo MCP Server exposes 14 tools through the Vinkius. Connect it to LangChain in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 14 Zenodo tools available for LangChain

When LangChain connects to Zenodo through Vinkius, your AI agent gets direct access to every tool listed below — spanning open-access, research-data, metadata-management, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

create

Create deposition on Zenodo

You can optionally provide metadata. Create a new Zenodo deposition

delete

Delete deposition on Zenodo

Note: Only unpublished depositions can be deleted. Delete an unpublished Zenodo deposition

delete

Delete deposition file on Zenodo

Delete a file from a Zenodo deposition

discard

Discard deposition on Zenodo

Discard edits on a Zenodo deposition

edit

Edit deposition on Zenodo

Edit a published Zenodo deposition

get

Get deposition on Zenodo

Retrieve a Zenodo deposition by ID

get

Get record on Zenodo

Retrieve a published Zenodo record by ID

list

List deposition files on Zenodo

List files in a Zenodo deposition

list

List depositions on Zenodo

List Zenodo depositions

list

List records on Zenodo

Search published Zenodo records

new

New version deposition on Zenodo

Create a new version of a Zenodo deposition

publish

Publish deposition on Zenodo

WARNING: Once published, a deposition cannot be deleted. Publish a Zenodo deposition

update

Update deposition on Zenodo

Update a Zenodo deposition

upload

Upload deposition file on Zenodo

Upload a text file to a Zenodo deposition

Connect Zenodo to LangChain via MCP

Follow these steps to wire Zenodo into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 14 tools from Zenodo via MCP

Why Use LangChain with the Zenodo MCP Server

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

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

Zenodo + LangChain Use Cases

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

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

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

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

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

Example Prompts for Zenodo in LangChain

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

"Search for public Zenodo records related to 'machine learning in healthcare'."

"Create a new Zenodo deposition for a dataset titled 'Global Temperature Trends 2023'."

"List all files currently attached to my deposition with ID 987654."

Troubleshooting Zenodo MCP Server with LangChain

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

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

Zenodo + LangChain FAQ

Common questions about integrating Zenodo 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.