How to Use the Zenodo MCP in LlamaIndex

Q: How does LlamaIndex handle searching with Zenodo?

You query records using listrecords. The resulting data is chunked and added to your vector store. Your agent then retrieves answers grounded in that actual, searchable API response.

Q: Can I search past deposition states using LlamaIndex and Zenodo?

Yes. By indexing the results of getdeposition or listdepositions, you can query historical versions and configurations from your knowledge base.

Q: What is the best way to track dataset changes with Zenodo using LlamaIndex?

You should index the output of versioning tools like newversiondeposition. This creates a timeline you can query semantically, answering questions about data evolution.

Q: Does this MCP Server support reading structured metadata with Zenodo and LlamaIndex?

It does. Tools like getrecord return structured metadata that LlamaIndex indexes perfectly. This allows you to perform complex queries on the dataset's properties.

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Connect Zenodo MCP to LlamaIndex

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

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Setup Zenodo with LlamaIndex

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Indexing Research Data with MCP Server

Don't just call the tools; index them. When you use `get_record` or `list_records`, LlamaIndex takes the output and turns it into a searchable vector chunk. This means your agent can answer questions based on actual Zenodo data, not just general knowledge. This creates a powerful RAG application where live API results become part of your persistent, queryable index.

Tracking Deposition History via LlamaIndex

Need to know how a dataset evolved? Call `list_depositions` and then use `get_deposition` for details. Indexing these historical snapshots lets you ask questions like, 'What was the scope of this data in March?' The system indexes version information via `new_version_deposition`, allowing deep semantic searches into your research lifecycle.

Managing Deposition Files with LlamaIndex

Files are indexed, too. Use `list_deposition_files` to see what's in a specific deposition. If you need the content for context, retrieving it via `get_deposition` and then indexing its contents grounds your answers. This is way better than having the LLM hallucinate file details; the answer comes straight from the Zenodo index.

Setup guide

Set up Zenodo MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all Zenodo MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to Zenodo tools.",
)
response = await agent.run("List recent Zenodo data")

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Common questions about Zenodo MCP in LlamaIndex

You query records using `list_records`. The resulting data is chunked and added to your vector store. Your agent then retrieves answers grounded in that actual, searchable API response.

Yes. By indexing the results of `get_deposition` or `list_depositions`, you can query historical versions and configurations from your knowledge base.

You should index the output of versioning tools like `new_version_deposition`. This creates a timeline you can query semantically, answering questions about data evolution.

It does. Tools like `get_record` return structured metadata that LlamaIndex indexes perfectly. This allows you to perform complex queries on the dataset's properties.

The server touches deposition metadata and file contents. When indexing, LlamaIndex treats both as searchable text chunks within your knowledge base.

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