ORCID (Researcher IDs) MCP Server for LlamaIndexGive LlamaIndex 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

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LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add ORCID (Researcher IDs) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The ORCID (Researcher IDs) MCP Server for LlamaIndex 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 llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to ORCID (Researcher IDs). "
            "You have 14 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in ORCID (Researcher IDs)?"
    )
    print(response)

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.

LlamaIndex agents combine ORCID (Researcher IDs) tool responses with indexed documents for comprehensive, grounded answers. Connect 14 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

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 LlamaIndex 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 LlamaIndex

When LlamaIndex 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 LlamaIndex via MCP

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

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 14 tools from ORCID (Researcher IDs)

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

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

Data-first architecture: LlamaIndex agents combine ORCID (Researcher IDs) tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain ORCID (Researcher IDs) tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query ORCID (Researcher IDs), a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what ORCID (Researcher IDs) tools were called, what data was returned, and how it influenced the final answer

ORCID (Researcher IDs) + LlamaIndex Use Cases

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

Hybrid search: combine ORCID (Researcher IDs) real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query ORCID (Researcher IDs) to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying ORCID (Researcher IDs) for fresh data

Analytical workflows: chain ORCID (Researcher IDs) queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for ORCID (Researcher IDs) in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex 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 LlamaIndex

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

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

ORCID (Researcher IDs) + LlamaIndex FAQ

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

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query ORCID (Researcher IDs) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.