Bring Transparency
to Pydantic AI
Learn how to connect Aracaju to Pydantic AI and start using 5 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the Aracaju MCP Server?
Connect to the Aracaju Transparency Portal to audit and analyze public data from the capital of Sergipe, Brazil. This server allows any AI agent to query municipal financial records and administrative data in real-time.
What you can do
- Revenues & Income — List and analyze municipality revenues by fiscal year and month to track tax collection and transfers.
- Public Spending — Query detailed expenses (despesas) by year or specific government bodies to monitor how public funds are allocated.
- Tenders & Bids — Access information on public tenders (licitações) to stay informed about government procurement processes.
- Contracts — Inspect signed contracts and agreements between the municipality and third parties.
- Personnel & Payroll — Retrieve data regarding public servants and payroll (servidores) to ensure administrative transparency.
How it works
- Subscribe to this server
- Enter 'PUBLIC' in the access field (this portal uses public data)
- Start querying municipal data from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Journalists & Researchers — quickly gather data for reports on public spending and municipal administration.
- Citizens & Activists — monitor government actions and fiscal responsibility directly through conversation.
- Legal & Compliance Professionals — verify public contracts and bidding processes without manual portal navigation.
Built-in capabilities (5)
List public tenders and bids (licitações)
List signed contracts (contratos)
List municipality expenses (despesas)
List public servants and payroll (servidores)
List municipality revenues (receitas)
Why Pydantic AI?
Pydantic AI validates every Aracaju tool response against typed schemas, catching data inconsistencies at build time. Connect 5 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.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Aracaju integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your Aracaju connection logic from agent behavior for testable, maintainable code
Aracaju in Pydantic AI
Aracaju and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect Aracaju to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for Aracaju in Pydantic AI
The Aracaju 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. All 5 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
Aracaju for Pydantic AI
Every tool call from Pydantic AI to the Aracaju MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How do I check the municipality's income for a specific month?
Use the list_revenues tool with the ano (year) and mes (month) parameters. The agent will return the total income and breakdown for that period.
Can I see who is on the public payroll?
Yes, use the list_personnel tool. You can filter by year and month to retrieve data regarding public servants and their respective payroll information.
Is it possible to monitor active public tenders?
Absolutely. Use the list_bids tool to get a comprehensive list of all public tenders and bidding processes currently recorded in the portal.
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 Aracaju MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
MCPServerHTTP not found
Update: pip install --upgrade pydantic-ai
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