How to Use the ANA (Movimentação de Dados) MCP in LangChain

Q: Can I trace ANA (Movimentação de Dados) tool calls with LangChain and LangSmith?

Yes, every tool invocation like getseriechuva or updateserieqa is automatically logged in LangSmith. You can inspect the exact inputs, outputs, and latency of each Brazilian water data request.

Build LangChain chains that fetch, verify, and update Brazilian water data in a single run.

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Connect ANA (Movimentação de Dados) MCP to LangChain

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Setup ANA (Movimentação de Dados) with LangChain

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Chain Brazilian Water Data in LangChain Agents

The `get_serie_vazao` and `get_serie_chuva` tools let your LangChain agents pull live river flow and rainfall numbers directly into your active execution chains. This MCP Server gives your agents direct access to the Brazilian water database. You can track every single step of these data-fetching decisions in LangSmith. Seeing exactly how your agent handles a rainfall spike before writing to the ANA database keeps your data pipelines reliable.

Cross-Reference Quality and Flow Series

The `get_serie_qa` tool pulls water quality metrics, while `get_serie_resumo_descarga` fetches discharge summaries to feed directly into your next chain link. Your agent evaluates both outputs to detect contamination risks during high-discharge events. Instead of writing custom API integration code, you let LangChain pass the output of one tool directly into the next. This setup makes complex hydrometeorological analysis feel like writing simple prompts.

Update River Profiles on the Fly

The `create_serie_perfil_transversal` and `update_serie_perfil_transversal` tools give your autonomous agents the power to modify cross-section profiles based on incoming field surveys. Your agent parses incoming files, extracts coordinates, and executes the updates automatically. Using the MCP Server adapter with LangGraph lets you build stateful loops that double-check profile data before committing changes. You get full control over how water level updates are processed.

Setup guide

Set up ANA (Movimentação de Dados) 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 ANA (Movimentação de Dados) 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({
    "ana-movimentacao-de-dados-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 ANA (Movimentação de Dados) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about ANA (Movimentação de Dados) MCP in LangChain

LangChain manages this by wrapping the client calls in custom runnables with exponential backoff. Since `get_serie_vazao` has no search limits, you can run large batch queries across multiple chains without hitting bottlenecks.

Yes, every tool invocation like `get_serie_chuva` or `update_serie_qa` is automatically logged in LangSmith. You can inspect the exact inputs, outputs, and latency of each Brazilian water data request.

You load this MCP Server alongside your other tools using the MultiServerMCPClient. Your LangChain agent can then query local weather APIs and immediately write rainfall data using `create_serie_chuva`.

The agent catches the error through standard LangChain error handling. If `update_serie_cota` fails due to a validation issue, the agent can analyze the error message and retry with corrected parameters.

All hydrometeorological data, including sensitive cross-section profiles and water quality series, runs inside an isolated V8 sandbox. Your credentials never touch public networks, and the connection remains entirely ephemeral.

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