Do I need an API key to use PubChem?

No. PubChem PUG REST is completely free and open without any authentication. The only limitation is a rate limit of 5 requests per second and 400 requests per minute, which is more than sufficient for conversational AI usage.

What molecular properties are returned for each compound?

Each compound includes: CID, IUPAC name, molecular formula, molecular weight, canonical SMILES, InChI identifier, XLogP (lipophilicity), hydrogen bond donor count, hydrogen bond acceptor count, and molecular complexity score. These cover Lipinski's Rule of Five for drug-likeness assessment.

Can I search by molecular formula instead of name?

Yes! Use the formula search tool with standard notation (e.g., C8H10N4O2 for caffeine, C9H8O4 for aspirin, H2O for water). PubChem will return all compounds matching that exact formula with their names and properties.

PubChem MCP Server for Pydantic AI 3 tools — connect in under 2 minutes

Name: PubChem
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 3 Tools SDK

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

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 PubChem "
            "(3 tools)."
        ),
    )

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

asyncio.run(main())

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About PubChem MCP Server

Connect your AI agent to PubChem — the world's largest open chemistry database, maintained by the National Center for Biotechnology Information (NCBI/NIH).

Pydantic AI validates every PubChem tool response against typed schemas, catching data inconsistencies at build time. Connect 3 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

Compound Search — Find chemical compounds by common name (aspirin, caffeine, glucose), IUPAC name, or CAS number across 116M+ indexed compounds
CID Lookup — Get comprehensive molecular data for any compound by its PubChem Compound ID including formula, weight, SMILES, InChI, and physicochemical properties
Formula Search — Find all compounds matching a specific molecular formula (e.g., C9H8O4 for aspirin)

The PubChem MCP Server exposes 3 tools through the Vinkius. Connect it to Pydantic AI in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect PubChem to Pydantic AI via MCP

Follow these steps to integrate the PubChem MCP Server with Pydantic AI.

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 3 tools from PubChem with type-safe schemas

Why Use Pydantic AI with the PubChem MCP Server

Pydantic AI provides unique advantages when paired with PubChem 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 PubChem integration code

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

Dependency injection system cleanly separates your PubChem connection logic from agent behavior for testable, maintainable code

PubChem + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the PubChem MCP Server delivers measurable value.

Type-safe data pipelines: query PubChem with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple PubChem tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query PubChem and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock PubChem responses and write comprehensive agent tests

PubChem MCP Tools for Pydantic AI (3)

These 3 tools become available when you connect PubChem to Pydantic AI via MCP:

get_pubchem_compound

Get full chemical data for a PubChem compound by CID

search_pubchem

Returns molecular formula, weight, SMILES, InChI, XLogP, hydrogen bond donors/acceptors, and complexity. Try: aspirin, caffeine, glucose, penicillin, dopamine. Search PubChem for chemical compounds by name

search_pubchem_formula

g. C9H8O4, C8H10N4O2, H2O) and find matching compounds. Find compounds by molecular formula

Example Prompts for PubChem in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with PubChem immediately.

"What are the molecular properties of aspirin?"

"Search for compounds with the molecular formula C8H10N4O2."

"Get the full chemical details for PubChem compound CID 5090."

Troubleshooting PubChem MCP Server with Pydantic AI

Common issues when connecting PubChem to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

PubChem + Pydantic AI FAQ

Common questions about integrating PubChem 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 PubChem MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.