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

Chain CORE (Open Access Research) tools directly into your LangChain agents for automated discovery of open access papers.

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Connect CORE (Open Access Research) MCP to LangChain

Create your Vinkius account to connect CORE (Open Access Research) to LangChain 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 LangChain

Ask AI about this MCP

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Claude

Perplexity

Build reasoning chains with CORE (Open Access Research)

Your LangChain agent uses `search_articles` to pull raw data into a pipeline. Each result feeds directly into the next step of your logic. This MCP server lets you define the exact sequence of operations. You control how your agent interprets metadata from `get_article` before it moves to the next action.

Trace every tool call in LangSmith

Every interaction with the CORE API is logged through LangSmith. You see exactly what your agent sent to `global_search` and how the data was formatted. Debugging becomes trivial when you can inspect the input and output of every tool call. You catch errors in your multi-step reasoning before they ripple through the rest of your chain.

Combine research with external databases

Integrate CORE repository data alongside your existing SQL or vector stores. Your agent decides whether to pull from a local database or run `search_repositories` based on the user prompt. This creates a unified workflow where research papers exist alongside your private datasets. The agent manages the context switches automatically as it builds your final response.

Setup guide

Set up CORE (Open Access Research) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes CORE (Open Access Research) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "core-open-access-research-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent CORE (Open Access Research) transactions"
    })
    print(result["messages"][-1].content)

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

Install the MCP adapters and point your client to the Vinkius endpoint. You initialize the connection and pass the tool list directly into your agent constructor.

Yes, you construct a chain that iterates through search results. The agent fetches abstracts, evaluates relevance, and compiles a summary without manual intervention.

The server remains stateless by default, but you can manage session persistence via the client session. This ensures your research history stays clean across multiple turns.

Use standard LangChain error handling wrappers around your tool definitions. Since the server returns structured data, you can implement retry logic if a request fails.

Vinkius handles all authentication via a single endpoint token. Your search queries and metadata remain isolated within your private session, away from other users.

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