Mapulus MCP Server for Pydantic AI 9 tools — connect in under 2 minutes
Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Mapulus 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 Mapulus "
"(9 tools)."
),
)
result = await agent.run(
"What tools are available in Mapulus?"
)
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 Mapulus MCP Server
Connect your Mapulus account to any AI agent and access deep Australian location intelligence through natural conversation.
Pydantic AI validates every Mapulus tool response against typed schemas, catching data inconsistencies at build time. Connect 9 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
- Statistical Boundaries — Search and list suburbs, postcodes, LGAs, and other Australian geographies
- Demographic Data — Retrieve ABS Census-derived insights on population, income, and housing
- Spatial Analytics — Generate isochrones (catchment areas) and query H3 hexagonal indices
- Location Enrichment — Enrich any lat/lon coordinate with detailed geographic and statistical context
The Mapulus MCP Server exposes 9 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 Mapulus to Pydantic AI via MCP
Follow these steps to integrate the Mapulus 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 9 tools from Mapulus with type-safe schemas
Why Use Pydantic AI with the Mapulus MCP Server
Pydantic AI provides unique advantages when paired with Mapulus 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 Mapulus integration code
Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
Dependency injection system cleanly separates your Mapulus connection logic from agent behavior for testable, maintainable code
Mapulus + Pydantic AI Use Cases
Practical scenarios where Pydantic AI combined with the Mapulus MCP Server delivers measurable value.
Type-safe data pipelines: query Mapulus with guaranteed response schemas, feeding validated data into downstream processing
API orchestration: chain multiple Mapulus tool calls with Pydantic validation at each step to ensure data integrity end-to-end
Production monitoring: build validated alert agents that query Mapulus and output structured, schema-compliant notifications
Testing and QA: use Pydantic AI's dependency injection to mock Mapulus responses and write comprehensive agent tests
Mapulus MCP Tools for Pydantic AI (9)
These 9 tools become available when you connect Mapulus to Pydantic AI via MCP:
enrich_location
Enrich a location with geographic context
get_boundary_details
g., "poa:2000"). Get details for a specific boundary
get_demographics
Get demographics for a boundary
get_h3_index
Get H3 index for a location
get_isochrone
Generate travel-time boundaries
get_postcode_data
Get data for a specific postcode
list_data_topics
List available data topics
search_boundaries
Search for Australian statistical boundaries
search_suburbs
Search specifically for Australian suburbs
Example Prompts for Mapulus in Pydantic AI
Ready-to-use prompts you can give your Pydantic AI agent to start working with Mapulus immediately.
"Search for boundaries matching 'Sydney'."
"Get demographics for postcode 2000."
"Show available data topics."
Troubleshooting Mapulus MCP Server with Pydantic AI
Common issues when connecting Mapulus to Pydantic AI through the Vinkius, and how to resolve them.
MCPServerHTTP not found
pip install --upgrade pydantic-aiMapulus + Pydantic AI FAQ
Common questions about integrating Mapulus 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 Mapulus 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 Mapulus to Pydantic AI
Get your token, paste the configuration, and start using 9 tools in under 2 minutes. No API key management needed.