Compatible with every major AI agent and IDE
What is the Zenodo MCP Server?
Connect your Zenodo account to any AI agent to streamline your scientific research workflows and data management through natural conversation.
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- Deposition Management — Create new unpublished depositions, update metadata, and manage your research drafts directly from the AI.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- Metadata Control — Precisely update titles, creators, descriptions, licenses, and access rights for your unpublished work.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.
How it works
- Subscribe to this server
- Enter your Zenodo Personal Access Token
- Start managing your research data from Claude, Cursor, or any MCP-compatible client
No more manual navigation through complex forms to update a dataset description or find a specific research record. Your AI acts as a dedicated research assistant.
Who is this for?
- Researchers & Academics — quickly draft depositions and manage metadata for publications without leaving your writing environment.
- Data Scientists — automate the listing and retrieval of datasets for analysis directly from your code editor.
- Open Science Advocates — easily search and discover public research artifacts to foster collaboration and transparency.
Built-in capabilities (14)
You can optionally provide metadata. Create a new Zenodo deposition
Note: Only unpublished depositions can be deleted. Delete an unpublished Zenodo deposition
Delete a file from a Zenodo deposition
Discard edits on a Zenodo deposition
Edit a published Zenodo deposition
Retrieve a Zenodo deposition by ID
Retrieve a published Zenodo record by ID
List files in a Zenodo deposition
List Zenodo depositions
Search published Zenodo records
Create a new version of a Zenodo deposition
WARNING: Once published, a deposition cannot be deleted. Publish a Zenodo deposition
Update a Zenodo deposition
Upload a text file to a Zenodo deposition
Why LlamaIndex?
LlamaIndex agents combine Zenodo tool responses with indexed documents for comprehensive, grounded answers. Connect 14 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
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Data-first architecture: LlamaIndex agents combine Zenodo tool responses with indexed documents for comprehensive, grounded answers
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Query pipeline framework lets you chain Zenodo tool calls with transformations, filters, and re-rankers in a typed pipeline
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Multi-source reasoning: agents can query Zenodo, a vector store, and a SQL database in a single turn and synthesize results
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Observability integrations show exactly what Zenodo tools were called, what data was returned, and how it influenced the final answer
Zenodo in LlamaIndex
Zenodo and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Zenodo to LlamaIndex through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Zenodo in LlamaIndex
The Zenodo 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. All 14 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in LlamaIndex only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.

* 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
How Vinkius secures
Zenodo for LlamaIndex
Every tool call from LlamaIndex to the Zenodo MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I delete a deposition that has already been published?
No. The delete_deposition tool only works for unpublished depositions. Once a record is published on Zenodo, it is permanent to ensure scientific traceability.
How can I search for public datasets about a specific topic?
You can use the list_records tool with a search query. For example, ask the agent to 'Search Zenodo records for climate change' and it will return matching public entries.
Is it possible to see which files are included in a deposition before downloading them?
Yes! Use the list_deposition_files tool with the Deposition ID. The agent will provide a list of all filenames and metadata associated with that specific deposition.
How does LlamaIndex connect to MCP servers?
Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.
Can I combine MCP tools with vector stores?
Yes. LlamaIndex agents can query Zenodo tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.
Does LlamaIndex support async MCP calls?
Yes. LlamaIndex's async agent framework supports concurrent MCP tool calls for high-throughput data processing pipelines.
BasicMCPClient not found
Install: pip install llama-index-tools-mcp
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