Do I need an API key to use Semantic Scholar?

No API key is required for basic usage. The public API provides 5,000 requests per 5 minutes shared among unauthenticated users. For higher throughput, academic and institutional users can request a free API key at semanticscholar.org, which grants dedicated rate limits of 1–10 requests per second depending on the endpoint.

What is the TLDR feature and how does it work?

TLDR (Too Long; Didn't Read) is an AI-generated one-sentence summary of each paper, powered by Allen AI's SciTLDR NLP model. It distills the key contribution or finding of a paper into a single, easily digestible sentence — ideal for quickly scanning relevance without reading an entire abstract or paper.

What is the difference between total citations and influential citations?

Total citations count every paper that references the work. Influential citations are a subset — papers where the cited work meaningfully contributes to the citing paper's research (not just a passing mention in the related work section). This metric is calculated by Semantic Scholar's AI models and provides a much more accurate measure of real scientific impact.

Semantic Scholar MCP Server for CrewAI 4 tools — connect in under 2 minutes

Name: Semantic Scholar
Availability: InStock
Author: Vinkius

docs.crewai.com/mcp/overview

Built by Vinkius GDPR 4 Tools Framework

Connect your CrewAI agents to Semantic Scholar through the Vinkius — pass the Edge URL in the `mcps` parameter and every Semantic Scholar tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Semantic Scholar Specialist",
    goal="Help users interact with Semantic Scholar effectively",
    backstory=(
        "You are an expert at leveraging Semantic Scholar tools "
        "for automation and data analysis."
    ),
    # Your Vinkius token — get it at cloud.vinkius.com
    mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)

task = Task(
    description=(
        "Explore all available tools in Semantic Scholar "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 4 available tools "
        "and what they can do."
    ),
)

crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)

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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).

When paired with CrewAI, Semantic Scholar becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Semantic Scholar tools autonomously — one agent queries data, another analyzes results, a third compiles reports — all orchestrated through the Vinkius with zero configuration overhead.

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 CrewAI 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 CrewAI via MCP

Follow these steps to integrate the Semantic Scholar MCP Server with CrewAI.

Install CrewAI

Run pip install crewai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Customize the agent

Adjust the role, goal, and backstory to fit your use case

Run the crew

Run python crew.py — CrewAI auto-discovers 4 tools from Semantic Scholar

Why Use CrewAI with the Semantic Scholar MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Semantic Scholar through the Model Context Protocol.

Multi-agent collaboration lets you decompose complex workflows into specialized roles — one agent researches, another analyzes, a third generates reports — each with access to MCP tools

CrewAI's native MCP integration requires zero adapter code: pass the Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime

Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls

Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports

Semantic Scholar + CrewAI Use Cases

Practical scenarios where CrewAI combined with the Semantic Scholar MCP Server delivers measurable value.

Automated multi-step research: a reconnaissance agent queries Semantic Scholar for raw data, then a second analyst agent cross-references findings and flags anomalies — all without human handoff

Scheduled intelligence reports: set up a crew that periodically queries Semantic Scholar, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Semantic Scholar tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow

Compliance and audit automation: a compliance agent queries Semantic Scholar against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Semantic Scholar MCP Tools for CrewAI (4)

These 4 tools become available when you connect Semantic Scholar to CrewAI 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 CrewAI

Ready-to-use prompts you can give your CrewAI 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 CrewAI

Common issues when connecting Semantic Scholar to CrewAI through the Vinkius, and how to resolve them.

MCP tools not discovered

Ensure the Edge URL is correct. CrewAI connects lazily when the crew starts — check console output.

Agent not using tools

Make the task description specific. Instead of "do something", say "Use the available tools to list contacts".

Timeout errors

CrewAI has a 10s connection timeout by default. Ensure your network can reach the Edge URL.

Rate limiting or 429 errors

The Vinkius enforces per-token rate limits. Check your subscription tier and request quota in the dashboard. Upgrade if you need higher throughput.

Semantic Scholar + CrewAI FAQ

Common questions about integrating Semantic Scholar MCP Server with CrewAI.

How does CrewAI discover and connect to MCP tools?

CrewAI connects to MCP servers lazily — when the crew starts, each agent resolves its MCP URLs and fetches the tool catalog via the standard tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.

Can different agents in the same crew use different MCP servers?

Yes. Each agent has its own mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.

What happens when an MCP tool call fails during a crew run?

CrewAI wraps tool failures as context for the agent. The LLM receives the error message and can decide to retry with different parameters, fall back to a different tool, or mark the task as partially complete. This resilience is critical for production workflows.

Can CrewAI agents call multiple MCP tools in parallel?

CrewAI agents execute tool calls sequentially within a single reasoning step. However, you can run multiple agents in parallel using process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.

Can I run CrewAI crews on a schedule (cron)?

Yes. CrewAI crews are standard Python scripts, so you can invoke them via cron, Airflow, Celery, or any task scheduler. The crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.