How to Use the Acre Dados Abertos MCP in LangChain

Chain Acre Dados Abertos data directly into your LangChain pipelines for automated reasoning.

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Connect Acre Dados Abertos MCP to LangChain

Create your Vinkius account to connect Acre Dados Abertos to LangChain and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Acre Dados Abertos with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Build multi-step logic with LangChain

Feed live government data into your agent chains. Use `search_datastore` to grab specific records and pipe that output into your next prompt. Your LangChain agent handles the flow. It decides when to trigger `get_package` or `list_groups` based on the context of your current task.

Trace your data calls

Watch every step your agent takes. LangSmith records the latency and input tokens whenever your chain invokes an MCP Server tool. Check exactly what `list_organizations` returned before the next step runs. You'll catch errors in your reasoning logic before they cascade.

Integrate stateful sessions

Connect this MCP Server to your persistent memory. Use client sessions so your agent remembers previous queries about Acre datasets. Link `search_packages` results with other databases in your chain. You get a unified view of state information without manual data munging.

Setup guide

Set up Acre Dados Abertos MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Acre Dados Abertos tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "acre-dados-abertos-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Acre Dados Abertos transactions"
    })
    print(result["messages"][-1].content)

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Why Choose Vinkius

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Built-in savings

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Common questions about Acre Dados Abertos MCP in LangChain

Install the MCP adapters and pass the tool list to your agent constructor. The agent will automatically identify the right tools to call based on your natural language request.

Yes. You can add this server alongside any other tool in your LangChain configuration. The agent manages the tool selection order.

It does. Use the client session object to maintain state across multiple turns in your conversation.

The agent receives an error response directly from the server. You can write fallback logic in your chain to handle the exception.

This server only provides read-only access to public Brazilian government records. No private user data is ever transmitted or stored.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.