ORCID (Researcher IDs) MCP Server for LangChainGive LangChain instant access to 14 tools to Add Item, Csv Search, Delete Item, and more

Name: ORCID (Researcher IDs)
Price: 29 USD
Availability: InStock
Rating: 4.9 (1 reviews)
Author: Vinkius

GDPR Free for Subscribers

LangChain is the leading Python framework for composable LLM applications. Connect ORCID (Researcher IDs) through Vinkius and LangChain agents can call every tool natively. combine them with retrievers, memory, and output parsers for sophisticated AI pipelines.

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The ORCID (Researcher IDs) MCP Server for LangChain is a standout in the Knowledge Management category — giving your AI agent 14 tools to work with, ready to go from day one.

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python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MultiServerMCPClient({
        "orcid-researcher-ids": {
            "transport": "streamable_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,
        )
        response = await agent.ainvoke({
            "messages": [{
                "role": "user",
                "content": "Using ORCID (Researcher IDs), show me what tools are available.",
            }]
        })
        print(response["messages"][-1].content)

asyncio.run(main())

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About ORCID (Researcher IDs) MCP Server

Connect the ORCID registry to your AI agent to seamlessly navigate the global ecosystem of researcher identifiers and scholarly records.

LangChain's ecosystem of 500+ components combines seamlessly with ORCID (Researcher IDs) through native MCP adapters. Connect 14 tools via Vinkius and use ReAct agents, Plan-and-Execute strategies, or custom agent architectures. with LangSmith tracing giving full visibility into every tool call, latency, and token cost.

What you can do

Registry Search — Perform standard or expanded Solr searches to find researchers by name, institution, or keywords using search and expanded_search.
Profile Summaries — Retrieve complete researcher records, including biographical details and activity summaries, via get_record and get_activities.
Works & Funding — Inspect specific research outputs and funding history using get_works or drill down into specific items with get_section_item.
Trust Markers — Access validated trust markers for records using get_summary (requires Member API).
Record Management — Add or update items in an ORCID record directly through the agent using add_item and update_item (requires Member API).

The ORCID (Researcher IDs) MCP Server exposes 14 tools through the Vinkius. Connect it to LangChain in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 14 ORCID (Researcher IDs) tools available for LangChain

When LangChain connects to ORCID (Researcher IDs) through Vinkius, your AI agent gets direct access to every tool listed below — spanning researcher-search, academic-profile, solr-search, 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.

add

Add item on ORCID (Researcher IDs)

Requires Member API access token with /activities/update or /person/update scope. Add a new item to an ORCID record (Member API only)

csv

Csv search on ORCID (Researcher IDs)

Search the ORCID registry and return CSV data

delete

Delete item on ORCID (Researcher IDs)

Requires Member API access token. Delete an item from an ORCID record (Member API only)

expanded

Expanded search on ORCID (Researcher IDs)

Search the ORCID registry (Expanded)

get

Get activities on ORCID (Researcher IDs)

Get summary of all activities for an ORCID record

get

Get person on ORCID (Researcher IDs)

Get biographical section of an ORCID record

get

Get record on ORCID (Researcher IDs)

Get full summary of an ORCID record

get

Get section item on ORCID (Researcher IDs)

Get full details for a specific item in an ORCID record

get

Get summary on ORCID (Researcher IDs)

Requires Member API access token. Get validated trust markers (Member API only)

get

Get works on ORCID (Researcher IDs)

Get summary of research works for an ORCID record

Register webhook on ORCID (Researcher IDs)

Requires /webhook scope. Register a webhook for an ORCID record (Premium Member API only)

action

Search on ORCID (Researcher IDs)

Search the ORCID registry (Standard)

unregister

Unregister webhook on ORCID (Researcher IDs)

Unregister a webhook for an ORCID record (Premium Member API only)

update

Update item on ORCID (Researcher IDs)

Requires Member API access token. Update an existing item in an ORCID record (Member API only)

Connect ORCID (Researcher IDs) to LangChain via MCP

Follow these steps to wire ORCID (Researcher IDs) into LangChain. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install langchain langchain-mcp-adapters langgraph langchain-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save the code and run python agent.py

Explore tools

The agent discovers 14 tools from ORCID (Researcher IDs) via MCP

Why Use LangChain with the ORCID (Researcher IDs) MCP Server

LangChain provides unique advantages when paired with ORCID (Researcher IDs) through the Model Context Protocol.

The largest ecosystem of integrations, chains, and agents. combine ORCID (Researcher IDs) MCP tools with 500+ LangChain components

Agent architecture supports ReAct, Plan-and-Execute, and custom strategies with full MCP tool access at every step

LangSmith tracing gives you complete visibility into tool calls, latencies, and token usage for production debugging

Memory and conversation persistence let agents maintain context across ORCID (Researcher IDs) queries for multi-turn workflows

ORCID (Researcher IDs) + LangChain Use Cases

Practical scenarios where LangChain combined with the ORCID (Researcher IDs) MCP Server delivers measurable value.

RAG with live data: combine ORCID (Researcher IDs) tool results with vector store retrievals for answers grounded in both real-time and historical data

Autonomous research agents: LangChain agents query ORCID (Researcher IDs), synthesize findings, and generate comprehensive research reports

Multi-tool orchestration: chain ORCID (Researcher IDs) tools with web scrapers, databases, and calculators in a single agent run

Production monitoring: use LangSmith to trace every ORCID (Researcher IDs) tool call, measure latency, and optimize your agent's performance

Example Prompts for ORCID (Researcher IDs) in LangChain

Ready-to-use prompts you can give your LangChain agent to start working with ORCID (Researcher IDs) immediately.

"Search the ORCID registry for researchers with the family name 'Einstein'."

"Get the biographical details for ORCID 0000-0002-1825-0097."

"List all research works for ORCID 0000-0003-1415-9265."

Troubleshooting ORCID (Researcher IDs) MCP Server with LangChain

Common issues when connecting ORCID (Researcher IDs) to LangChain through Vinkius, and how to resolve them.

MultiServerMCPClient not found

Install: pip install langchain-mcp-adapters

ORCID (Researcher IDs) + LangChain FAQ

Common questions about integrating ORCID (Researcher IDs) MCP Server with LangChain.

How does LangChain connect to MCP servers?

Use langchain-mcp-adapters to create an MCP client. LangChain discovers all tools and wraps them as native LangChain tools compatible with any agent type.

Which LangChain agent types work with MCP?

All agent types including ReAct, OpenAI Functions, and custom agents work with MCP tools. The tools appear as standard LangChain tools after the adapter wraps them.

Can I trace MCP tool calls in LangSmith?

Yes. All MCP tool invocations appear as traced steps in LangSmith, showing input parameters, response payloads, latency, and token usage.

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