São Paulo (Cidade) MCP Server for Pydantic AIGive Pydantic AI instant access to 11 tools to Datastore Search, Datastore Search Sql, Get Group, and more

Name: São Paulo (Cidade)
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 São Paulo (Cidade) 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 for Pydantic AI

Open in ChatGPT Open in Claude Open in Perplexity

The São Paulo (Cidade) MCP Server for Pydantic AI is a standout in the Data Analytics category — giving your AI agent 11 tools to work with, ready to go from day one.

Built for AI Agents by Vinkius

Vinkius delivers Streamable HTTP and SSE to any MCP client

Claude

ChatGPT

Cursor

Gemini

Windsurf

VS Code

JetBrains

Vercel

+ other MCP clients

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 São Paulo (Cidade) "
            "(11 tools)."
        ),
    )

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

asyncio.run(main())

Fully ManagedVinkius Servers

60%Token savings

High SecurityEnterprise-grade

IAMAccess control

EU AI ActCompliant

DLPData protection

V8 IsolateSandboxed

Ed25519Audit chain

<40msKill switch

Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 São Paulo (Cidade) MCP Server

Connect your AI agent directly to the São Paulo City Open Data Portal (CKAN). This server allows you to navigate thousands of public datasets covering health, education, transport, and finance in Brazil's largest city.

Pydantic AI validates every São Paulo (Cidade) tool response against typed schemas, catching data inconsistencies at build time. Connect 11 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

Dataset Discovery — Search for specific datasets using keywords or list all available packages in the portal.
Granular Inspection — Fetch detailed metadata for datasets and individual resources (files) to understand data structures.
Advanced Data Querying — Perform searches within data stores or execute complex SQL queries directly on CSV-backed resources.
Organizational Mapping — List and inspect city secretariats (organizations) and thematic groups to find relevant data sources.
Tag Exploration — Browse datasets by tags to discover related public information across different departments.

The São Paulo (Cidade) MCP Server exposes 11 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 11 São Paulo (Cidade) tools available for Pydantic AI

When Pydantic AI connects to São Paulo (Cidade) through Vinkius, your AI agent gets direct access to every tool listed below — spanning open-data, urban-data, public-administration, 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.

datastore

Datastore search on São Paulo (Cidade)

Search data within a resource

datastore

Datastore search sql on São Paulo (Cidade)

SQL Query on a resource

get

Get group on São Paulo (Cidade)

Get group details

get

Get organization on São Paulo (Cidade)

Get organization details

get

Get package on São Paulo (Cidade)

Get dataset details

get

Get resource on São Paulo (Cidade)

Get resource details

list

List groups on São Paulo (Cidade)

g., Educação, Meio Ambiente). List groups (themes)

list

List organizations on São Paulo (Cidade)

g., Secretarias) that own datasets. List organizations

list

List packages on São Paulo (Cidade)

List all datasets (packages) in the portal

list

List tags on São Paulo (Cidade)

List tags

Search packages on São Paulo (Cidade)

Search datasets

Connect São Paulo (Cidade) to Pydantic AI via MCP

Follow these steps to wire São Paulo (Cidade) 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 11 tools from São Paulo (Cidade) with type-safe schemas

Why Use Pydantic AI with the São Paulo (Cidade) MCP Server

Pydantic AI provides unique advantages when paired with São Paulo (Cidade) 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 São Paulo (Cidade) integration code

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

Dependency injection system cleanly separates your São Paulo (Cidade) connection logic from agent behavior for testable, maintainable code

São Paulo (Cidade) + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the São Paulo (Cidade) MCP Server delivers measurable value.

Type-safe data pipelines: query São Paulo (Cidade) with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple São Paulo (Cidade) tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query São Paulo (Cidade) and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock São Paulo (Cidade) responses and write comprehensive agent tests

Example Prompts for São Paulo (Cidade) in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with São Paulo (Cidade) immediately.

"Search for datasets about public transport in São Paulo."

"List all organizations that provide open data in the portal."

"Get the details for the dataset 'folha-de-pagamento'."

Troubleshooting São Paulo (Cidade) MCP Server with Pydantic AI

Common issues when connecting São Paulo (Cidade) to Pydantic AI through Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

São Paulo (Cidade) + Pydantic AI FAQ

Common questions about integrating São Paulo (Cidade) 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 São Paulo (Cidade) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.