Bring Open Science
to CrewAI
Learn how to connect Figshare to CrewAI and start using 20 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Figshare MCP Server?
Connect your Figshare account to any AI agent to streamline your research data management and publication workflows through natural conversation.
What you can do
- Article Management — List public articles, fetch specific article details, and create, update, or delete private articles in your account.
- File Handling — List files associated with articles, initiate multi-part S3 uploads, and track file details for your research datasets.
- Collections & Projects — Create and list public collections and projects to organize your scholarly output effectively.
- Metadata Control — Update titles, descriptions, and other metadata for your articles to ensure they are discoverable and well-documented.
How it works
- Subscribe to this server
- Enter your Figshare Personal Access Token
- Start managing your research repository from Claude, Cursor, or any MCP-compatible client
No more manual navigation through repository interfaces to check upload statuses or update metadata. Your AI acts as a research data manager.
Who is this for?
- Researchers & Academics — quickly upload datasets, update article descriptions, and check publication statuses without leaving your workspace.
- Data Managers — automate the organization of large collections and projects across institutional repositories.
- Librarians & Archivists — query public research outputs and manage metadata updates efficiently.
Built-in capabilities (20)
Complete a file upload
Create a new collection
Create a new private article
Create a new project
Delete an article
Get details of a specific article
Get total downloads for an article
Get total views for an article
Get custom metadata fields for the institution
Get file details
Get HR feed upload details
Initiate a file upload for an article
List files for an article
List public articles
List public collections
List public projects
Advanced search for articles
Advanced search for collections
Advanced search for projects
Update an existing article
Why CrewAI?
When paired with CrewAI, Figshare becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Figshare tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
- —
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
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CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the
mcpsparameter and agents auto-discover every available tool at runtime - —
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
- —
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Figshare in CrewAI
Figshare and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Figshare to CrewAI 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 Figshare in CrewAI
The Figshare 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 20 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in CrewAI 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
Figshare for CrewAI
Every tool call from CrewAI to the Figshare MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
Can I upload files to a Figshare article using this integration?
Yes. You can use initiate_file_upload to start the process and complete_file_upload once the data transfer is finished. You can also use list_article_files to verify the contents of any article.
How do I create a new private draft for my research?
Simply ask the agent to use the create_private_article tool. Provide a title and an optional description, and the agent will generate a new private entry in your Figshare account.
Is it possible to organize my articles into projects or collections?
Absolutely. You can use create_project or create_collection to set up organizational structures, and use list_public_projects or list_public_collections to browse existing ones.
How does CrewAI discover and connect to MCP tools?
CrewAI connects to MCP servers lazily. when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.
Can different agents in the same crew use different MCP servers?
Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.
What happens when an MCP tool call fails during a crew run?
CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.
Can CrewAI agents call multiple MCP tools in parallel?
CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.
Can I run CrewAI crews on a schedule (cron)?
Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.
MCP tools not discovered
Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts. check console output.
Agent not using tools
Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".
Timeout errors
CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.
Rate limiting or 429 errors
Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.
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