How to Use the CORE (Open Access Research) MCP in CrewAI

Q: How do I assign CORE (Open Access Research) tools to a CrewAI agent?

You pass the Vinkius MCP endpoint to the mcps list when defining your CrewAI agent. The agent automatically gains access to searcharticles and other academic research tools.

Q: How do I limit which CORE (Open Access Research) tools my CrewAI crew can use?

You can use the MCPServerHTTP class from crewai.mcp and apply a toolfilter. This restricts the crew to specific tools like getarticlepdf while hiding administrative tools.

Q: What happens if a CORE search returns zero results in CrewAI?

The agent executing globalsearch will receive an empty list and use its internal reasoning to adjust the search terms. It will then retry the search with broader academic keywords.

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Connect CORE (Open Access Research) MCP to CrewAI

Create your Vinkius account to connect CORE (Open Access Research) to CrewAI and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup CORE (Open Access Research) with CrewAI

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Cooperative Research Teams with CrewAI

This MCP Server exposes `search_articles` to your research crew, allowing specialized agents to divide and conquer academic literature. One agent searches the database while another analyzes the findings. Your coordinator agent uses `get_article` to pull deep metadata for papers flagged as high-priority. This shared context lets the entire crew work with the exact same academic data.

Autonomous Journal Validation and Auditing

The `get_journal` tool allows your auditing agent to verify the publication status of any paper found during a sweep. The agent checks the ISSN against the official CORE registry. Concurrently, a repository agent runs `search_repositories` to find alternative open-access mirrors. The crew compiles these sources into a single, verified bibliography.

Persistent Academic History Tracking

The `get_article_history` tool enables your archiving agent to track revisions and updates for specific papers over time. This keeps your local research library synchronized with the live CORE database. The crew uses `resolve_oai` to reconcile older academic records with modern digital object identifiers. This step runs autonomously in the background without requiring manual data entry.

Setup guide

Set up CORE (Open Access Research) MCP in CrewAI

Prerequisites

Python 3.10+ installed
crewai package (pip install crewai)
Active Vinkius subscription with a valid endpoint token

1

Install CrewAI
Run pip install crewai to install the framework. MCP support is built-in via the mcps parameter.
2

Add the MCP URL to your agent
Pass your Vinkius endpoint directly to the mcps list. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. CrewAI handles tool discovery and caching automatically.
3

Kick off your crew
Create a Crew with your agent and tasks. Call crew.kickoff() — the agent will automatically invoke CORE (Open Access Research) tools as needed.

crew.py

from crewai import Agent, Task, Crew

agent = Agent(
    role="CORE (Open Access Research) Analyst",
    goal="Access and analyze CORE (Open Access Research) data via MCP.",
    backstory="Expert analyst with direct CORE (Open Access Research) access.",
    mcps=[
        "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ],
)

task = Task(
    description="List recent CORE (Open Access Research) transactions",
    agent=agent,
    expected_output="A summary of recent activity",
)

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

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Prerequisites

Python 3.10+ installed
crewai + crewai-tools packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install crewai crewai-tools. The MCPServerAdapter handles lifecycle management and tool conversion.
2

Connect with MCPServerAdapter
Use MCPServerAdapter as a context manager with SseServerParameters pointing to your Vinkius endpoint. The adapter automatically manages connection lifecycle.
3

Assign tools and run
Pass the returned mcp_tools to your agent's tools parameter. The adapter converts MCP tools to native BaseTool objects compatible with all CrewAI agents.

crew.py

from crewai import Agent, Task, Crew
from crewai_tools import MCPServerAdapter
from mcp import SseServerParameters

server_params = SseServerParameters(
    url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)

with MCPServerAdapter(server_params) as mcp_tools:
    agent = Agent(
        role="CORE (Open Access Research) Analyst",
        goal="Access and analyze CORE (Open Access Research) data via MCP.",
        backstory="Expert analyst with direct CORE (Open Access Research) access.",
        tools=mcp_tools,
    )

    task = Task(
        description="List recent CORE (Open Access Research) transactions",
        agent=agent,
        expected_output="A summary of recent activity",
    )

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

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by CORE. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about CORE (Open Access Research) MCP in CrewAI

You pass the Vinkius MCP endpoint to the `mcps` list when defining your CrewAI agent. The agent automatically gains access to `search_articles` and other academic research tools.

Yes, CrewAI's shared memory allows the researcher agent to pass the outputs of `get_article` directly to the writer. The writer agent then drafts summaries using the verified metadata.

You can use the `MCPServerHTTP` class from `crewai.mcp` and apply a `tool_filter`. This restricts the crew to specific tools like `get_article_pdf` while hiding administrative tools.

The agent executing `global_search` will receive an empty list and use its internal reasoning to adjust the search terms. It will then retry the search with broader academic keywords.

All data passing through the MCP Server is handled in an ephemeral V8 sandbox. Your research queries, journal ISSNs, and downloaded paper metadata are never cached or stored.

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