CrossRef MCP Server for LlamaIndex 3 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add CrossRef as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
import asyncio
from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
mcp_tool_spec = McpToolSpec(client=mcp_client)
tools = await mcp_tool_spec.to_tool_list_async()
agent = FunctionAgent(
tools=tools,
llm=OpenAI(model="gpt-4o"),
system_prompt=(
"You are an assistant with access to CrossRef. "
"You have 3 tools available."
),
)
response = await agent.run(
"What tools are available in CrossRef?"
)
print(response)
asyncio.run(main())
* 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
About CrossRef MCP Server
Give your AI agent direct access to the world's largest registry of scholarly metadata — 140M+ records spanning every DOI ever assigned across all scientific publishers.
LlamaIndex agents combine CrossRef tool responses with indexed documents for comprehensive, grounded answers. Connect 3 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.
What you can do
- Universal Search — Find any published work across journals, books, conference papers, datasets, and dissertations using free-text queries
- DOI Resolution — Instant metadata lookup for any DOI with title, complete author list, journal, year, type, and citation count
- Author Discovery — Search for all publications by a specific researcher name across all major publishers simultaneously
The CrossRef MCP Server exposes 3 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
How to Connect CrossRef to LlamaIndex via MCP
Follow these steps to integrate the CrossRef MCP Server with LlamaIndex.
Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token
Run the agent
Save to agent.py and run: python agent.py
Explore tools
The agent discovers 3 tools from CrossRef
Why Use LlamaIndex with the CrossRef MCP Server
LlamaIndex provides unique advantages when paired with CrossRef through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine CrossRef tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain CrossRef tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query CrossRef, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what CrossRef tools were called, what data was returned, and how it influenced the final answer
CrossRef + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the CrossRef MCP Server delivers measurable value.
Hybrid search: combine CrossRef real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query CrossRef to augment indexed data with live information before generating user-facing responses
Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying CrossRef for fresh data
Analytical workflows: chain CrossRef queries with LlamaIndex's data connectors to build multi-source analytical reports
CrossRef MCP Tools for LlamaIndex (3)
These 3 tools become available when you connect CrossRef to LlamaIndex via MCP:
get_crossref_doi
g. 10.1038/nature12373) and get complete metadata: title, all authors, journal, year, type, citation count, and abstract. Look up any scholarly work by its DOI
search_crossref
Every result includes DOI, citation count, and full bibliographic data. The world's largest DOI registry. Search 140M+ scholarly works across all scientific disciplines
search_crossref_author
Returns their publications sorted by relevance with citation counts. Find publications by a specific author
Example Prompts for CrossRef in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with CrossRef immediately.
"Look up the paper with DOI 10.1038/nature12373 and show me all its details."
"Find all publications by Jennifer Doudna related to gene editing."
"Search CrossRef for the latest research on quantum computing error correction."
Troubleshooting CrossRef MCP Server with LlamaIndex
Common issues when connecting CrossRef to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpCrossRef + LlamaIndex FAQ
Common questions about integrating CrossRef MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect CrossRef with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect CrossRef to LlamaIndex
Get your token, paste the configuration, and start using 3 tools in under 2 minutes. No API key management needed.