Drupal MCP Server for Pydantic AI 10 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Drupal through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
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 Drupal "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Drupal?"
)
print(result.data)
asyncio.run(main())
* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure
About Drupal MCP Server
Connect your Drupal site to any AI agent and take full control of your headless content management and JSON:API workflows through natural conversation.
Pydantic AI validates every Drupal tool response against typed schemas, catching data inconsistencies at build time. Connect 10 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
- Content Node Orchestration — Identify bounded routing spaces inside headless Drupal models and extract explicitly attached REST arrays tracking standard JSON:API boundaries
- Entity Management — Provision highly-available JSON payloads to write rows into Drupal entities, or irreversibly vaporize nodes to clear live document entities
- Revision Mutation — Substitute draft values safely by triggering HTTP PATCH operations to replace row segments isolating partial updates mapping specific UUIDs
- Taxonomy Management — Enumerate explicitly attached structured rules defining how content is categorized natively through terms and vocabularies
- File & Media Discovery — Taps raw configurations pulling explicitly managed Drupal files to discover raw CDN URIs mitigating headless media blocking
- Identity Oversight — Evaluate physical arrays isolating actual editors and administrators exposing their metadata and access roles securely
- Metadata Auditing — Retrieve the exact structural matching verifying file storage and analyzing explicit UUID bounds for managed attachments
The Drupal MCP Server exposes 10 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 Drupal to Pydantic AI via MCP
Follow these steps to integrate the Drupal 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 10 tools from Drupal with type-safe schemas
Why Use Pydantic AI with the Drupal MCP Server
Pydantic AI provides unique advantages when paired with Drupal 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 Drupal integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Drupal connection logic from agent behavior for testable, maintainable code
Drupal + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Drupal MCP Server delivers measurable value.
Type-safe data pipelines: query Drupal with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Drupal tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Drupal and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Drupal responses and write comprehensive agent tests
Drupal MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Drupal to Pydantic AI via MCP:
create_cms_node
Provision a highly-available JSON Payload writing Rows into Drupal entities
get_file_metadata
Retrieve the exact structural matching verifying File storage
get_single_node
Retrieve explicit Cloud logging tracing explicit Node UUIDs
get_taxonomy_term
Perform structural extraction of properties driving active Term mappings
list_content_nodes
Identify bounded routing spaces inside the Headless Drupal models
list_drupal_users
Identify precise active arrays spanning rented Admin identities
list_managed_files
Inspect deep internal arrays mitigating specific Picture constraints
list_term_vocabularies
Enumerate explicitly attached structured rules exporting Taxonomies
patch_cms_node
Mutate global Web CRM boundaries substituting Draft values safely
wipe_cms_node
Irreversibly vaporize explicit App nodes dropping live Document entities
Example Prompts for Drupal in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Drupal immediately.
"List the last 5 'article' nodes from Drupal"
"Create an 'article' with title 'AI Integration' and body 'Testing Drupal MCP...'"
"Show me all terms in the 'tags' vocabulary"
Troubleshooting Drupal MCP Server with Pydantic AI
Common issues when connecting Drupal to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiDrupal + Pydantic AI FAQ
Common questions about integrating Drupal MCP Server with Pydantic AI.
How does Pydantic AI discover MCP tools?
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?
Can I switch LLM providers without changing MCP code?
Connect Drupal with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
AI-first code editor with integrated LLM-powered coding assistance.
GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
Autonomous AI coding agent that runs inside VS Code.
Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect Drupal to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.