Directus 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 Directus 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 Directus "
"(10 tools)."
),
)
result = await agent.run(
"What tools are available in Directus?"
)
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 Directus MCP Server
Connect your Directus instance to any AI agent and take full control of your open-source data platform and headless CMS through natural conversation.
Pydantic AI validates every Directus 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
- Collection Orchestration — Identify bounded routing spaces inside headless Directus SQL mappers and extract database tables traversing collections natively
- Item Management — Provision highly-available JSON payloads to write or update Directus rows, or irreversibly wipe records to clear internal database allocations
- Schema Auditing — Enumerate explicitly attached structured rules defining your PostgreSQL tables and execute bulk iterations to track registered system types
- Metadata Inspection — Analyze specific localized variables decoding native collection boundaries and extracting hidden tracking configurations seamlessly
- Field Discovery — Inspect deep internal arrays defining precisely which fields accept formatting and validate payloads strictly against your DB links
- Identity Oversight — Explains explicitly mapped profile arrays iterating the exact users authorized within the DB layer enforcing RBAC boundaries securely
- Media Storage — Retrieve the exact structural matching verifying file uploads and generating download routes for active frontends
The Directus 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 Directus to Pydantic AI via MCP
Follow these steps to integrate the Directus 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 Directus with type-safe schemas
Why Use Pydantic AI with the Directus MCP Server
Pydantic AI provides unique advantages when paired with Directus 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 Directus integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Directus connection logic from agent behavior for testable, maintainable code
Directus + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Directus MCP Server delivers measurable value.
Type-safe data pipelines: query Directus with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Directus tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Directus and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Directus responses and write comprehensive agent tests
Directus MCP Tools for Pydantic AI (10)
These 10 tools become available when you connect Directus to Pydantic AI via MCP:
create_cms_record
Provision a highly-available JSON Payload writing Directus Rows
get_collection_details
Perform structural extraction of properties driving active Tables
get_single_item
Retrieve explicit Cloud logging tracing explicit DB Row UUIDs
list_collection_fields
Inspect deep internal arrays mitigating specific Column configurations
list_collection_items
Identify bounded routing spaces inside Headless Directus SQL mappers
list_directus_files
Retrieve the exact structural matching verifying Media storage
list_directus_users
Identify precise active arrays spanning rented Admin identities
list_schema_collections
Enumerate explicitly attached structured rules defining PostgreSQL tables
patch_cms_record
Mutate global Web CRM boundaries substituting Database values via ID
wipe_cms_record
Irreversibly vaporize explicit App nodes dropping live Rows
Example Prompts for Directus in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Directus immediately.
"List all items in the 'articles' collection"
"Create a new record in 'products': {'name': 'Gaming Mouse', 'price': 50}"
"Show me the schema for the 'orders' table"
Troubleshooting Directus MCP Server with Pydantic AI
Common issues when connecting Directus to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiDirectus + Pydantic AI FAQ
Common questions about integrating Directus 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 Directus 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 Directus to Pydantic AI
Get your token, paste the configuration, and start using 10 tools in under 2 minutes. No API key management needed.