MIT DBLP MCP Server for CrewAIGive CrewAI instant access to 16 tools to Get Author, Get Author Publications, Get Author Stats, and more

Name: MIT DBLP
Price: 29 USD
Availability: InStock
Rating: 2.2 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

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

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The MIT DBLP MCP Server for CrewAI is a standout in the Knowledge Management 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="MIT DBLP Specialist",
    goal="Help users interact with MIT DBLP effectively",
    backstory=(
        "You are an expert at leveraging MIT DBLP 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 MIT DBLP "
        "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 MIT DBLP MCP Server

Connect to the DBLP Computer Science Bibliography — the most comprehensive index of CS research, maintained by Schloss Dagstuhl.

When paired with CrewAI, MIT DBLP becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call MIT DBLP 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

Full-Text Search — Search 6M+ CS publications across all venues
Author Profiles — Explore researcher profiles and publication histories
Venue Browsing — Search conferences (NeurIPS, ICML, SIGMOD, OSDI) and journals (JACM, TOCS)
Co-Author Networks — Discover collaboration patterns between researchers
AI/ML Papers — Dedicated search for NeurIPS, ICML, ICLR, and AAAI papers
Systems Papers — Dedicated search for OSDI, SOSP, SIGCOMM, NSDI papers
Theory Papers — Dedicated search for STOC, FOCS, SODA papers
Database Papers — Dedicated search for SIGMOD, VLDB, ICDE papers
Author Statistics — Publication counts, venue distribution, and year-over-year trends

The MIT DBLP 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 MIT DBLP tools available for CrewAI

When CrewAI connects to MIT DBLP through Vinkius, your AI agent gets direct access to every tool listed below — spanning academic-research, bibliography, computer-science, 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.

get

Get author on MIT DBLP

The PID can be found in DBLP URLs (e.g. for "https://dblp.org/pid/b/YoshuaBengio" the PID is "b/YoshuaBengio"). Get author profile by DBLP PID

get

Get author publications on MIT DBLP

Returns up to 40 most recent publications with full metadata. Use the author name as it appears on DBLP. Get all publications by a specific author

get

Get author stats on MIT DBLP

Essential for evaluating research productivity and impact. Get publication statistics for an author

get

Get coauthors on MIT DBLP

Returns a ranked list of collaborators ordered by number of joint publications. Essential for understanding research collaboration patterns. Get co-author network of a researcher

get

Get publication on MIT DBLP

g. "journals/cacm/Knuth74", "conf/nips/VaswaniSPUJGKP17"). The key uniquely identifies every record in DBLP. Get publication details by DBLP key

get

Get venue on MIT DBLP

Use conference abbreviations (ICML, NeurIPS, SIGMOD) or full journal names. Get venue details (conference or journal)

get

Get venue publications on MIT DBLP

Essential for exploring what was published at a particular conference edition (e.g. NeurIPS 2024). Get papers published at a specific venue

Search ai papers on MIT DBLP

These are the premier conferences for artificial intelligence and machine learning research. Search AI and machine learning papers at top venues

Search authors on MIT DBLP

Returns author names, DBLP profile URLs, and disambiguation notes. DBLP meticulously disambiguates authors with the same name. Search computer science authors on DBLP

Search by year on MIT DBLP

Useful for tracking research trends over time or finding papers from a specific conference edition. Search publications filtered by year

Search database papers on MIT DBLP

Search database papers at top venues

Search in venue on MIT DBLP

Combine a venue name with an optional topic query to find relevant papers at a particular venue. Search for papers within a specific venue

Search publications on MIT DBLP

Covers all major conferences (NeurIPS, ICML, SIGMOD, VLDB, OSDI) and journals (JACM, TOCS, VLDBJ). Returns titles, authors, venues, years, DOIs, and DBLP keys. Search 6M+ computer science publications on DBLP

Search systems papers on MIT DBLP

Search systems papers at top venues

Search theory papers on MIT DBLP

Search theoretical CS papers at top venues

Search venues on MIT DBLP

Returns venue names, DBLP URLs, and types. Search CS conferences and journals

Connect MIT DBLP to CrewAI via MCP

Follow these steps to wire MIT DBLP 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 MIT DBLP

Why Use CrewAI with the MIT DBLP MCP Server

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

MIT DBLP + CrewAI Use Cases

Practical scenarios where CrewAI combined with the MIT DBLP MCP Server delivers measurable value.

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

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

Example Prompts for MIT DBLP in CrewAI

Ready-to-use prompts you can give your CrewAI agent to start working with MIT DBLP immediately.

"Find recent AI papers on large language models at NeurIPS"

"Search for publications by Yoshua Bengio"

"Find the latest database systems papers from SIGMOD and VLDB"

Troubleshooting MIT DBLP MCP Server with CrewAI

Common issues when connecting MIT DBLP 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.

MIT DBLP + CrewAI FAQ

Common questions about integrating MIT DBLP 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.