Stanford Semantic Scholar MCP Server for CrewAIGive CrewAI instant access to 16 tools to Batch Get Authors, Batch Get Papers, Bulk Search Papers, and more

Name: Stanford Semantic Scholar
Price: 29 USD
Availability: InStock
Rating: 0.2 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

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The Stanford Semantic Scholar MCP Server for CrewAI is a standout in the Education category — giving your AI agent 16 tools to work with, ready to go from day one.

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python

from crewai import Agent, Task, Crew

agent = Agent(
    role="Stanford Semantic Scholar Specialist",
    goal="Help users interact with Stanford Semantic Scholar effectively",
    backstory=(
        "You are an expert at leveraging Stanford 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 Stanford Semantic Scholar "
        "and summarize their capabilities."
    ),
    agent=agent,
    expected_output=(
        "A detailed summary of 16 available tools "
        "and what they can do."
    ),
)

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

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About Stanford Semantic Scholar MCP Server

Connect to the Semantic Scholar Academic Graph API and unlock the world's largest free academic knowledge graph.

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

What you can do

Paper Search — Full-text search across 200M+ papers with filters for year, field of study, venue, and open access
Citation Analysis — Navigate forward citations (who cited this?) and backward references (what did this cite?)
Author Profiles — Search and retrieve author metrics including h-index, paper count, and citation count
Batch Operations — Retrieve multiple papers or authors in a single request for efficient analysis
AI Recommendations — Get machine learning-powered paper recommendations from single or multiple seed papers
Venue Filtering — Search within specific conferences (NeurIPS, ICML, CVPR) or journals (Nature, Science, Cell)
Field Filtering — Search within specific fields: Computer Science, Medicine, Biology, Physics, and 20+ more

The Stanford Semantic Scholar MCP Server exposes 16 tools through the Vinkius. Connect it to CrewAI in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 16 Stanford Semantic Scholar tools available for CrewAI

When CrewAI connects to Stanford Semantic Scholar through Vinkius, your AI agent gets direct access to every tool listed below — spanning semantic-scholar, academic-papers, citations, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

batch

Batch get authors on Stanford Semantic Scholar

Returns names, affiliations, paper counts, citation counts, and h-indices. Useful for comparing researchers or building collaboration network analyses. Retrieve multiple author profiles in a single request

batch

Batch get papers on Stanford Semantic Scholar

Accepts S2 IDs, DOIs, ArXiv IDs, or PubMed IDs. Useful for comparing papers, building reading lists, or analyzing a set of related works. Retrieve multiple papers in a single request

bulk

Bulk search papers on Stanford Semantic Scholar

Each call returns a batch of results plus a continuation token. Pass the token in subsequent calls to get the next batch. Ideal for systematic literature reviews and meta-analyses. Bulk search for large result sets with token pagination

get

Get author on Stanford Semantic Scholar

Returns name, affiliations, homepage, external IDs (DBLP, ORCID), total paper count, citation count, and h-index. The definitive tool for understanding a researcher's academic impact. Get author profile with h-index, citations, and metrics

get

Get author papers on Stanford Semantic Scholar

Returns papers with titles, years, venues, citation counts, open access status, and fields of study. Essential for reviewing a researcher's body of work or finding specific publications by a known author. Get all papers by a specific author

get

Get multi recommendations on Stanford Semantic Scholar

The algorithm finds papers similar to the positive set but dissimilar to the negative set. Ideal for focused literature discovery. Get recommendations from multiple seed papers with positive/negative signals

get

Get paper on Stanford Semantic Scholar

Accepts multiple ID formats: Semantic Scholar ID (e.g. "649def34f8be52c8b66281af98ae884c09aef38b"), DOI (e.g. "10.1038/s41586-021-03819-2"), ArXiv ID (e.g. "arXiv:2106.09685"), PubMed ID (e.g. "PMID:34845388"), or ACL ID (e.g. "ACL:W12-3903"). Returns title, abstract, authors, venue, year, citation counts, open access PDF URL, and publication metadata. Get full paper details by ID, DOI, ArXiv ID, or PubMed ID

get

Get paper authors on Stanford Semantic Scholar

Useful for identifying research leaders and collaboration networks. Get authors of a specific paper with h-index and metrics

get

Get paper citations on Stanford Semantic Scholar

This is essential for understanding a paper's impact, finding follow-up work, and tracing how an idea has evolved. Returns citing paper metadata including titles, venues, years, and citation counts. Get papers that cite a given paper

get

Get paper references on Stanford Semantic Scholar

Essential for literature reviews, understanding the intellectual lineage of a work, and finding foundational papers in a research area. Get papers referenced by a given paper

get

Get recommendations on Stanford Semantic Scholar

The algorithm analyzes citation patterns, co-citation networks, and content similarity to find the most relevant papers you should read next. This is the AI-native way to discover related literature. Get AI-powered paper recommendations from a seed paper

match

Match paper title on Stanford Semantic Scholar

Uses fuzzy matching to handle slight variations. Returns the best matching paper with a match score. Ideal when you have a paper title from a reference list or bibliography and need to find its full metadata. Find an exact paper match from a title string

Search authors on Stanford Semantic Scholar

Returns author profiles with affiliations, paper counts, citation counts, and h-index. Use this to find researchers in a specific field, discover top contributors, or find collaborators. Search authors by name across the academic graph

Search by field on Stanford Semantic Scholar

Supported fields: Computer Science, Medicine, Biology, Chemistry, Physics, Mathematics, Engineering, Environmental Science, Economics, Business, Political Science, Sociology, Psychology, Art, History, Geography, Philosophy, Materials Science, Geology, Linguistics, Education, Agricultural and Food Sciences, Law. Search papers filtered by field of study

Search by venue on Stanford Semantic Scholar

Use venue names like "Nature", "Science", "NeurIPS", "ICML", "CVPR", "ACL", "EMNLP", "The Lancet", "JAMA", "Cell", "Physical Review Letters". Essential for tracking publications in specific top-tier venues. Search papers filtered by conference or journal

Search papers on Stanford Semantic Scholar

Returns titles, venues, years, citation counts, open access status, fields of study, and authors. Supports filtering by year range (e.g. "2020-2024"), fields of study (e.g. "Computer Science"), venue (e.g. "Nature"), and open access availability. Search across 200M+ academic papers by keyword

Connect Stanford Semantic Scholar to CrewAI via MCP

Follow these steps to wire Stanford Semantic Scholar into CrewAI. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

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 16 tools from Stanford Semantic Scholar

Why Use CrewAI with the Stanford Semantic Scholar MCP Server

CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Stanford 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 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

Stanford Semantic Scholar + CrewAI Use Cases

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

Automated multi-step research: a reconnaissance agent queries Stanford 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 Stanford Semantic Scholar, analyzes trends over time, and generates executive briefings in markdown or PDF format

Multi-source enrichment pipelines: chain Stanford 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 Stanford Semantic Scholar against predefined policy rules, generates deviation reports, and routes findings to the appropriate team

Example Prompts for Stanford Semantic Scholar in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with Stanford Semantic Scholar immediately.

"Find the most cited papers on transformer architectures published since 2020"

"What is Geoffrey Hinton's h-index and how many papers has he published?"

"Recommend papers similar to "Attention Is All You Need""

Troubleshooting Stanford Semantic Scholar MCP Server with CrewAI

Common issues when connecting Stanford Semantic Scholar to CrewAI through 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

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

Stanford Semantic Scholar + CrewAI FAQ

Common questions about integrating Stanford 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.