Does it support both NFS-e and NF-e?

Yes! The tools allow emitting and managing both Service Invoices (nfse) and Product Invoices (nfe) seamlessly.

How do I get my API Token?

Log in to the Focus NFe portal and navigate to the API section to generate your token.

Can I download the authorized XML?

Yes, use the `download_xml` tool with the document reference to retrieve the full XML content.

Is it homologated for all Brazilian cities?

Focus NFe covers thousands of cities for NFS-e emission. You can check their API documentation for the complete list of supported IBGE city codes.

Focus NFe MCP Server for Pydantic AI 5 tools — connect in under 2 minutes

Name: Focus NFe
Availability: InStock
Author: Vinkius

ai.pydantic.dev/mcp

Built by Vinkius GDPR 5 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Focus NFe through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

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 Focus NFe "
            "(5 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Focus NFe?"
    )
    print(result.data)

asyncio.run(main())

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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 Focus NFe MCP Server

Connect Focus NFe to any AI agent and automate your Brazilian fiscal documentation — issue service invoices (NFS-e) and product invoices (NF-e), check status, cancel documents, and download XMLs via natural conversation.

Pydantic AI validates every Focus NFe 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.

What you can do

Emit NFS-e — Issue Service Invoices instantly
Emit NF-e — Issue Product Invoices with full item details
Consult Status — Check if a document is authorized, cancelled, or processing
Cancel Documents — Cancel issued invoices with a justification reason
Download XML — Retrieve the authorized XML for accounting and storage

The Focus NFe MCP Server exposes 5 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 Focus NFe to Pydantic AI via MCP

Follow these steps to integrate the Focus NFe 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 5 tools from Focus NFe with type-safe schemas

Why Use Pydantic AI with the Focus NFe MCP Server

Pydantic AI provides unique advantages when paired with Focus NFe 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 Focus NFe integration code

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

Dependency injection system cleanly separates your Focus NFe connection logic from agent behavior for testable, maintainable code

Focus NFe + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Focus NFe MCP Server delivers measurable value.

Type-safe data pipelines: query Focus NFe with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Focus NFe tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Focus NFe and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Focus NFe responses and write comprehensive agent tests

Focus NFe MCP Tools for Pydantic AI (5)

These 5 tools become available when you connect Focus NFe to Pydantic AI via MCP:

cancel_document

Cancel an authorized invoice

consult_document

Consult the status and details of an invoice

download_xml

Download the authorized XML document

emit_nfe

Emit a Product Invoice (NF-e)

emit_nfse

Emit a Service Invoice (NFS-e)

Example Prompts for Focus NFe in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI agent to start working with Focus NFe immediately.

"Emit a service invoice for R$500 for 'Consulting Services'."

"Cancel invoice REF-123 with reason 'Client requested cancellation'."

"Get the XML for invoice REF-123."

Troubleshooting Focus NFe MCP Server with Pydantic AI

Common issues when connecting Focus NFe to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Focus NFe + Pydantic AI FAQ

Common questions about integrating Focus NFe 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 Focus NFe MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.