How to Use the Aracaju MCP in LangChain

Q: Can I combine Aracaju payroll tools with other LangChain agents?

Yes, you can pass the output of listpersonnel to any other agent in your graph. This lets you cross-reference municipal salaries with external economic datasets.

Feed Aracaju municipal finances directly into your LangChain runs to build traceable public spending audit chains.

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Connect Aracaju MCP to LangChain

Create your Vinkius account to connect Aracaju 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 Aracaju with LangChain

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Claude

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Track Aracaju spending using LangChain chains

The `list_expenses` tool pulls live municipal outflow data directly into your active run. Your chain inspects raw transaction records, matching them against specific departments without manual CSV exports. By feeding this raw data into LangChain's pipeline, you can pass the output of one step straight to the next tool. You get immediate verification of where municipal money goes.

Link bids to contracts via this MCP Server

The `list_bids` tool retrieves public tenders, which your agent instantly feeds into `list_contracts` to verify winning vendors. This MCP Server lets your agent trace the entire procurement cycle from initial posting to final signature. Your agent handles the multi-step reasoning required to flag mismatched values. You get a clear picture of municipal procurement without writing custom API integration code.

Inspect Aracaju payroll runs inside LangSmith

The `list_personnel` tool fetches public servant salary details that flow through your chains. LangSmith logs these inputs and outputs, showing you exactly how your agent parses complex Brazilian payroll data. You can monitor token usage and latency for every single payroll query. This setup ensures your transparency audits remain highly visible and easy to debug.

Setup guide

Set up Aracaju 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 Aracaju 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({
    "aracaju-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 Aracaju transactions"
    })
    print(result["messages"][-1].content)

Get your connection token →

Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by Aracaju Transparency. All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about Aracaju MCP in LangChain

You manage this by configuring the LangChain MCP adapter with custom backoff settings. Since the server pulls municipal data, rate-limiting your chain prevents the city's endpoints from blocking your requests.

Yes, you can pass the output of `list_personnel` to any other agent in your graph. This lets you cross-reference municipal salaries with external economic datasets.

Install the adapter package and instantiate the client pointing to the Vinkius URL. Then, call the tool getter to load the transparency tools into your agent.

Yes, the tools allow you to pass specific parameters to filter revenues and expenses. You can narrow down the data to specific months or years directly from your prompt.

The server only accesses public transparency data like revenues and public contracts, meaning no private citizen financial records are exposed. Vinkius runs the server inside an isolated sandbox, ensuring your queries remain confidential.

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