How to Use the CrossRef MCP in LlamaIndex

Q: Can I use CrossRef tool outputs to build a vector index in LlamaIndex?

Yes, you can. The outputs from searchcrossref are structured JSON, which you can load directly into document nodes and index for semantic search.

Q: Does the CrossRef tool spec in LlamaIndex support tool filtering?

It does. You can use the allowedtools filter on the MCP client to restrict your agent to specific operations like author lookup.

Index 140 million scholarly works directly into your LlamaIndex vector store for zero-hallucination academic RAG.

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Connect CrossRef MCP to LlamaIndex

Create your Vinkius account to connect CrossRef 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 CrossRef with LlamaIndex

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Index paper metadata using DOIs in LlamaIndex

The `get_crossref_doi` tool retrieves verified metadata for any digital object identifier. It bypasses unreliable web scraping by pulling titles, authors, and abstracts directly from the source registry. LlamaIndex ingest pipelines turn this structured data into searchable document nodes. You can query past sessions and get answers grounded in actual metadata, not model hallucinations.

Build academic RAG pipelines with this MCP Server

The `search_crossref` tool queries the world's largest registry of scholarly works. It pulls back titles, DOIs, and citation counts for millions of papers across all scientific disciplines. This integration lets your indexer pull down real-time publication lists. LlamaIndex then embeds these results into your vector store, combining live API data and static documents into a unified query engine.

Track researcher portfolios for semantic search

The `search_crossref_author` tool finds publications associated with a specific researcher. It returns their full bibliography sorted by relevance alongside citation counts to help you build author profiles. Your query engine can use these lists to map out a researcher's academic trajectory. LlamaIndex indexes these bibliographies, allowing users to run semantic searches over an author's entire body of work.

Setup guide

Set up CrossRef 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 CrossRef 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 CrossRef tools.",
)
response = await agent.run("List recent CrossRef 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 CrossRef. 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 CrossRef MCP in LlamaIndex

Install llama-index-tools-mcp via pip. Initialize the BasicMCPClient with the Vinkius URL, wrap it in a McpToolSpec, and pass the tools to your indexer or agent.

Yes, you can. The outputs from search_crossref are structured JSON, which you can load directly into document nodes and index for semantic search.

It does. You can use the allowed_tools filter on the MCP client to restrict your agent to specific operations like author lookup.

No, this server focuses strictly on metadata, DOIs, and citation counts. It provides the precise bibliographic records you need to locate the full text elsewhere.

Vinkius processes all requests through isolated, zero-trust V8 sandboxes. Your search terms and requested DOIs are never cached or exposed to third parties.

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