Semantic Scholar MCP Server for LlamaIndex 4 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add Semantic Scholar as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.
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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 Semantic Scholar. "
"You have 4 tools available."
),
)
response = await agent.run(
"What tools are available in Semantic Scholar?"
)
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 Semantic Scholar MCP Server
Connect your AI agent to the world's most AI-enhanced academic knowledge graph, built and maintained by the Allen Institute for AI (AI2).
LlamaIndex agents combine Semantic Scholar tool responses with indexed documents for comprehensive, grounded answers. Connect 4 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
- AI-Powered Search — Find papers across 200M+ works with AI-generated TLDR summaries that distill each paper into a single sentence of key insight
- Influential Citations — Beyond simple citation count, see how many influential citations a paper has received — those that meaningfully build upon the cited work
- Multi-Format Lookup — Access papers by Semantic Scholar ID, DOI, ArXiv ID (arXiv:2106.09685), or PubMed ID (PMID:12345)
- Citation Graph — Explore the full citation chain of any paper, with TLDR summaries for each citing work
- Researcher Profiles — Find academics by name with paper counts, total citations, and h-index metrics
The Semantic Scholar MCP Server exposes 4 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 Semantic Scholar to LlamaIndex via MCP
Follow these steps to integrate the Semantic Scholar 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 4 tools from Semantic Scholar
Why Use LlamaIndex with the Semantic Scholar MCP Server
LlamaIndex provides unique advantages when paired with Semantic Scholar through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine Semantic Scholar tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain Semantic Scholar tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query Semantic Scholar, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what Semantic Scholar tools were called, what data was returned, and how it influenced the final answer
Semantic Scholar + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the Semantic Scholar MCP Server delivers measurable value.
Hybrid search: combine Semantic Scholar real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query Semantic Scholar 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 Semantic Scholar for fresh data
Analytical workflows: chain Semantic Scholar queries with LlamaIndex's data connectors to build multi-source analytical reports
Semantic Scholar MCP Tools for LlamaIndex (4)
These 4 tools become available when you connect Semantic Scholar to LlamaIndex via MCP:
get_semantic_citations
Essential for literature reviews and impact analysis. Find papers that cite a specific work on Semantic Scholar
get_semantic_paper
Accepts Semantic Scholar paper ID, DOI, ArXiv ID (e.g. arXiv:2106.09685), or PMID (e.g. PMID:12345). Get full paper details from Semantic Scholar by paper ID or DOI
search_semantic_author
Returns paper count, total citations, and h-index for each researcher. Find researchers and their publication metrics on Semantic Scholar
search_semantic_scholar
Returns papers with AI-generated TLDR summaries, citation counts, influential citation counts, and fields of study. Covers Computer Science, Medicine, Biology, Physics, and all STEM fields. Search 200M+ academic papers with AI-powered TLDR summaries and influence scores
Example Prompts for Semantic Scholar in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with Semantic Scholar immediately.
"What are the most cited papers on transformer architecture in deep learning?"
"Get the full details of the LoRA paper using its ArXiv ID arXiv:2106.09685."
"Find the researcher Yann LeCun and show me his publication metrics."
Troubleshooting Semantic Scholar MCP Server with LlamaIndex
Common issues when connecting Semantic Scholar to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpSemantic Scholar + LlamaIndex FAQ
Common questions about integrating Semantic Scholar 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 Semantic Scholar 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 Semantic Scholar to LlamaIndex
Get your token, paste the configuration, and start using 4 tools in under 2 minutes. No API key management needed.