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

Q: Can I use ANA (Movimentação de Dados) tools as a query engine in LlamaIndex?

Yes, you can convert the getseriechuva and getserievazao tools into a query engine using the LlamaIndex tool spec. This allows your RAG pipeline to fetch live Brazilian rainfall data during a user query.

Q: How does LlamaIndex handle semantic search over ANA (Movimentação de Dados) water quality records?

Your agent pulls the water quality data using getserieqa, processes it into document nodes, and indexes them. Users can then ask natural language questions about water safety, and LlamaIndex retrieves the exact matching records.

Q: Is it possible to filter which ANA (Movimentação de Dados) tools LlamaIndex can access?

Yes, you can use the allowedtools filter in your LlamaIndex MCP client setup. This lets you restrict your agent to read-only tools like getseriecota while blocking update tools in public-facing search apps.

Q: How do I handle large datasets of rainfall series in my index?

You can fetch the series using getseriechuva and use LlamaIndex chunking strategies to split the data. This keeps your vector embeddings precise and prevents your LLM context window from overflowing.

Index live Brazilian hydrometeorological data directly into your LlamaIndex vector stores for ground-truth RAG.

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Connect ANA (Movimentação de Dados) MCP to LlamaIndex

Create your Vinkius account to connect ANA (Movimentação de Dados) to LlamaIndex 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 ANA (Movimentação de Dados) with LlamaIndex

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Index Live ANA Water Data with LlamaIndex

The `get_serie_vazao` and `get_serie_cota` tools allow your LlamaIndex pipeline to pull river flow and stage levels directly into your vector index using this MCP Server. This lets your RAG applications query historical and live water levels without relying on outdated static files. Your agent uses these live metrics to answer complex hydrometeorological queries. This setup ensures that your agent answers are grounded in actual database values rather than AI hallucinations.

Build a Searchable Water Quality Archive

The `get_serie_qa` tool fetches water quality parameters that can be indexed on the fly. LlamaIndex takes this raw data and structures it so your agent can perform semantic searches over past water quality records. By combining this MCP Server with vector storage, you create a searchable memory of river health. Your agent can instantly compare current QA metrics against years of historical data.

Update and Index River Cross-Sections

The `update_serie_perfil_transversal` and `get_serie_perfil_transversal` tools let your index stay synchronized with physical river changes. When a new profile is uploaded, your LlamaIndex agent updates the database and immediately updates the corresponding index vectors. This loop guarantees that your engineering agents are always working with the latest cross-section geometries. You avoid the risk of running hydraulic simulations on stale profile data.

Setup guide

Set up ANA (Movimentação de Dados) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all ANA (Movimentação de Dados) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to ANA (Movimentação de Dados) tools.",
)
response = await agent.run("List recent ANA (Movimentação de Dados) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by ANA (Agência Nacional de Águas). 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 ANA (Movimentação de Dados) MCP in LlamaIndex

Yes, you can convert the `get_serie_chuva` and `get_serie_vazao` tools into a query engine using the LlamaIndex tool spec. This allows your RAG pipeline to fetch live Brazilian rainfall data during a user query.

Your agent pulls the water quality data using `get_serie_qa`, processes it into document nodes, and indexes them. Users can then ask natural language questions about water safety, and LlamaIndex retrieves the exact matching records.

Yes, you can use the allowed_tools filter in your LlamaIndex MCP client setup. This lets you restrict your agent to read-only tools like `get_serie_cota` while blocking update tools in public-facing search apps.

You can fetch the series using `get_serie_chuva` and use LlamaIndex chunking strategies to split the data. This keeps your vector embeddings precise and prevents your LLM context window from overflowing.

Absolutely, your water flow, rainfall, and quality data are processed within a zero-trust environment. The Vinkius platform handles all authentication tokens securely, ensuring that no raw API credentials are leaked during indexing.

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