How to Use the Figshare MCP in LangChain

Q: How do I handle multi-step file uploads using Figshare with LangChain?

Your LangChain agent can coordinate initiatefileupload and completefileupload in a single run. The agent grabs the upload URL from the first tool and passes it directly to the next step in your chain.

Q: Can a LangChain agent update metadata on private Figshare articles?

Yes, your LangChain agent can use the updatearticle tool to modify draft details. You can feed raw metadata from other chain steps directly into this tool to ensure compliance before publishing.

Q: How do I search institutional records using Figshare and LangChain?

Your LangChain agent can call searcharticles or searchprojects to query the repository. The search results are returned as structured data that your LangChain chains can immediately parse.

Q: Can I manage projects and collections with this setup?

You can use createproject and createcollection to organize your research outputs. This MCP integration allows your LangChain agent to group related drafts together automatically based on grant numbers.

Connect the Figshare MCP Server to your LangChain pipelines to automate file staging and metadata verification.

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Connect Figshare MCP to LangChain

Create your Vinkius account to connect Figshare to LangChain 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 Figshare with LangChain

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Automate Figshare uploads in LangChain chains

The Figshare MCP Server lets your LangChain agent run the entire multi-step upload pipeline by chaining `initiate_file_upload` and `complete_file_upload` directly. Because LangChain tracks every tool input and output, your agent can verify the payload using `list_article_files` to ensure no corrupted files enter your repository.

Validate institutional compliance in LangChain

The `get_custom_fields` tool provides your LangChain agent with your university's specific metadata requirements to compare against draft data. This automated gatekeeping prevents incomplete drafts from cluttering up your repository, keeping your LangChain workflows clean and your library curators happy.

Track research impact with LangChain runs

The `get_article_downloads` and `get_article_views` tools let your LangChain agent pull raw impact metrics across your entire collection. It turns static Figshare records into an active feed of real-time usage data for your LangChain application to parse.

Setup guide

Set up Figshare MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Figshare tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "figshare-mcp": {
        "transport": "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,
    )
    result = await agent.ainvoke({
        "messages": "List recent Figshare transactions"
    })
    print(result["messages"][-1].content)

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Figshare. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Figshare MCP in LangChain

Your LangChain agent can coordinate `initiate_file_upload` and `complete_file_upload` in a single run. The agent grabs the upload URL from the first tool and passes it directly to the next step in your chain.

Yes, your LangChain agent can use the `update_article` tool to modify draft details. You can feed raw metadata from other chain steps directly into this tool to ensure compliance before publishing.

Your LangChain agent can call `search_articles` or `search_projects` to query the repository. The search results are returned as structured data that your LangChain chains can immediately parse.

You can use `create_project` and `create_collection` to organize your research outputs. This MCP integration allows your LangChain agent to group related drafts together automatically based on grant numbers.

Your raw research datasets and metadata remain secure because all transactions run through Vinkius's isolated sandbox. This MCP Server only communicates using your designated token, meaning your private drafts stay private until your LangChain agent explicitly chooses to publish them.

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