How to Use the Unpaywall (Open Access Papers) MCP in LlamaIndex

Q: How does LlamaIndex help with Unpaywall (Open Access Papers)?

LlamaIndex takes the structured output from getarticleoastatus and embeds it into a vector store. This turns transient API results into persistent, searchable knowledge.

Index Unpaywall (Open Access Papers) data into a vector store for semantic search using LlamaIndex.

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Connect Unpaywall (Open Access Papers) MCP to LlamaIndex

Create your Vinkius account to connect Unpaywall (Open Access Papers) 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 Unpaywall (Open Access Papers) with LlamaIndex

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Build an OA Knowledge Index

LlamaIndex allows you to index the results from `get_article_oa_status` into a searchable knowledge base. When your agent runs this tool, the output metadata becomes part of your vector store. Later queries can then retrieve answers grounded in actual API data—like specific OA links—instead of relying on general model hallucinations.

Query Past Article Availability

You don't have to re-run the lookup every time. Because LlamaIndex indexes the output, you can query past sessions or configurations about article availability using semantic search. This means if a user asks a question based on DOIs checked last week, your agent pulls the actual OA status from the index.

Consolidate Scholarly Link Data

The `get_article_oa_status` tool gathers crucial full-text PDF links and Open Access metadata via a DOI. Indexing these results centralizes your knowledge. This allows you to build RAG applications that combine live API data with documents, all accessible through one unified queryable index.

Setup guide

Set up Unpaywall (Open Access Papers) 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 Unpaywall (Open Access Papers) 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 Unpaywall (Open Access Papers) tools.",
)
response = await agent.run("List recent Unpaywall (Open Access Papers) data")

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Common questions about Unpaywall (Open Access Papers) MCP in LlamaIndex

LlamaIndex takes the structured output from `get_article_oa_status` and embeds it into a vector store. This turns transient API results into persistent, searchable knowledge.

Yes. By indexing the tool's output, you can query past runs of `get_article_oa_status` and retrieve specific details about article availability from previous API calls.

The index stores the Open Access status, the original DOI used, and the resulting full-text PDF links provided by the MCP Server. This makes all metadata queryable.

Absolutely. It's built to combine external sources—like the output of `get_article_oa_status`—with your own internal document corpus into one queryable index.

This server deals with scholarly article metadata, specifically DOIs and the resulting Open Access status records. These structured details form the core of your indexed knowledge.

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