ORCID (Researcher IDs) MCP Server for Pydantic AIGive Pydantic AI 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

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect ORCID (Researcher IDs) through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

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The ORCID (Researcher IDs) MCP Server for Pydantic AI 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 pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to ORCID (Researcher IDs) "
            "(14 tools)."
        ),
    )

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

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.

Pydantic AI validates every ORCID (Researcher IDs) tool response against typed schemas, catching data inconsistencies at build time. Connect 14 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI

When Pydantic AI 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 Pydantic AI via MCP

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

Install Pydantic AI

Run pip install pydantic-ai

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) with type-safe schemas

Why Use Pydantic AI with the ORCID (Researcher IDs) MCP Server

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

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your ORCID (Researcher IDs) integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your ORCID (Researcher IDs) connection logic from agent behavior for testable, maintainable code

ORCID (Researcher IDs) + Pydantic AI Use Cases

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

Type-safe data pipelines: query ORCID (Researcher IDs) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple ORCID (Researcher IDs) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query ORCID (Researcher IDs) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock ORCID (Researcher IDs) responses and write comprehensive agent tests

Example Prompts for ORCID (Researcher IDs) in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

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

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

ORCID (Researcher IDs) + Pydantic AI FAQ

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

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your ORCID (Researcher IDs) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.

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