How to Use the Acre Dados Abertos MCP in LlamaIndex

Index Acre Dados Abertos records into your LlamaIndex knowledge base.

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Connect Acre Dados Abertos MCP to LlamaIndex

Create your Vinkius account to connect Acre Dados Abertos 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 Acre Dados Abertos with LlamaIndex

Ask AI about this MCP

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Claude

Perplexity

Ground your LlamaIndex answers

Turn API responses into searchable vector data. When you call `get_resource`, LlamaIndex stores the result in your index for future retrieval. You stop guessing and start querying actual government statistics. Your knowledge base stays current with the live state data.

Automate data indexing

Fetch metadata using `list_packages` and let the framework handle the embedding process. Your agents query this index to answer complex questions. This approach ensures that your RAG application references real-time facts. It prevents the model from hallucinating Acre-specific details.

Filter tools for precision

Restrict your agent to a subset of tools. You might only expose `search_datastore` to ensure the agent stays within specific record boundaries. Control the scope of your LlamaIndex operations easily. This keeps your search results clean and relevant to your specific research goal.

Setup guide

Set up Acre Dados Abertos 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 Acre Dados Abertos 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 Acre Dados Abertos tools.",
)
response = await agent.run("List recent Acre Dados Abertos data")

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Common questions about Acre Dados Abertos MCP in LlamaIndex

Use the MCP tool spec to pull the data into your agent. Once retrieved, you can pipe those outputs directly into your vector store.

By indexing the tool output, your answers are grounded in the data returned by the server. The agent relies on the retrieved context rather than its training data.

Absolutely. Once the tool outputs are indexed, you can perform semantic searches across the entire dataset metadata.

The MCP tool spec supports asynchronous execution. This keeps your indexing pipelines fast and responsive.

The server only touches public administrative data from the Acre government. Your local vector store remains under your control at all times.

Use it with your favorite AI tools

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

OpenAI Agents SDK sdk-python