How to Use the Figshare MCP in LlamaIndex

Q: How do I index public Figshare data into LlamaIndex?

You can use listpubliccollections or listpublicarticles to retrieve raw metadata. LlamaIndex then parses this structured text and converts it into searchable vector nodes for RAG pipelines.

Q: Can LlamaIndex agents search private Figshare projects?

Yes, by calling searchprojects with your authorized token, the LlamaIndex agent can retrieve private project details. The agent indexes these private records so you can query them alongside your public files.

Q: How do I retrieve file details from Figshare using LlamaIndex?

Use the getfiledetails tool within your retrieval pipeline. This allows your LlamaIndex agent to fetch specific file sizes and hashes to verify data integrity before indexing.

Q: Can I track article downloads and views with this setup?

You can call getarticledownloads and getarticleviews to monitor engagement. Running these MCP tools lets you query LlamaIndex to find your most impactful research datasets instantly.

Index Figshare research outputs directly into your LlamaIndex knowledge bases using this MCP Server.

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Connect Figshare MCP to LlamaIndex

Create your Vinkius account to connect Figshare 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 Figshare with LlamaIndex

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Index Figshare metadata with LlamaIndex

The Figshare MCP Server lets you pull raw metadata via `list_public_articles` and index it directly into LlamaIndex vector stores. By feeding `get_article` outputs into your index, you build a searchable knowledge base of active research for your LlamaIndex application.

Query private research folders in LlamaIndex

The `list_article_files` tool allows your LlamaIndex application to query private folders and ingest the contents into your local vector database. This setup allows researchers to cross-reference active files with older, published papers using LlamaIndex retrieval pipelines.

Organize collections based on semantic similarity

The `search_projects` tool lets your LlamaIndex agent search existing work and determine where a new draft belongs based on conceptual overlap. Once it finds the right fit, LlamaIndex executes `create_project` or `create_collection` to file the new dataset away automatically.

Setup guide

Set up Figshare 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 Figshare 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 Figshare tools.",
)
response = await agent.run("List recent Figshare data")

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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 LlamaIndex

You can use `list_public_collections` or `list_public_articles` to retrieve raw metadata. LlamaIndex then parses this structured text and converts it into searchable vector nodes for RAG pipelines.

Yes, by calling `search_projects` with your authorized token, the LlamaIndex agent can retrieve private project details. The agent indexes these private records so you can query them alongside your public files.

Use the `get_file_details` tool within your retrieval pipeline. This allows your LlamaIndex agent to fetch specific file sizes and hashes to verify data integrity before indexing.

You can call `get_article_downloads` and `get_article_views` to monitor engagement. Running these MCP tools lets you query LlamaIndex to find your most impactful research datasets instantly.

Your draft files and custom metadata are safe because all tool execution is sandboxed. The MCP Server acts as a secure bridge, passing your data directly to your local LlamaIndex pipeline without storing it externally.

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