How to Use the Inep Dados Abertos MCP in Google ADK

Q: How do I connect the Inep Dados Abertos MCP Server to my Google ADK agent?

You initialize McpToolset with the Vinkius connection parameters in your Python code. Pass this toolset directly to your LlmAgent to expose all 12 educational data tools.

Q: Can I restrict my Google ADK agent to only use specific Inep Dados Abertos tools?

Yes, you can use the optional toolnames filter when setting up your McpToolset. This prevents Gemini from executing heavy SQL queries if you only want it to list datasets.

Q: Does Inep Dados Abertos support BigQuery integration through Google ADK?

While the MCP Server queries the INEP API directly using searchdatastoresql, your agent can easily write those results to BigQuery. You write the Python logic to bridge the MCP tool output to your cloud database.

Feed live Brazilian educational metrics into your Google ADK pipelines for long-context Gemini reasoning.

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Connect Inep Dados Abertos MCP to Google ADK

Create your Vinkius account to connect Inep Dados Abertos to Google ADK 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 Inep Dados Abertos with Google ADK

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Run SQL on Brazilian educational data using Google ADK

The `search_datastore_sql` tool translates natural language questions into raw SQL queries that execute directly on the INEP datastore via your Google ADK agent. Because Gemini models support massive context windows, your Google ADK pipelines can process detailed tables returned by this Inep Dados Abertos MCP Server.

Discover INEP organizations and groups on the fly

The `list_organizations` and `get_organization` tools let your Google ADK agent identify which Brazilian departments published specific Inep Dados Abertos datasets. Using `list_groups` alongside `get_group` allows your Google ADK agent to categorize educational records by topic.

Filter resources dynamically in enterprise pipelines

The `search_resources` tool allows your Google ADK agent to locate specific CSV and Excel files within the Inep Dados Abertos MCP Server catalog. After finding the correct resource, the Google ADK agent uses `search_datastore` to extract the exact rows needed.

Setup guide

Set up Inep Dados Abertos MCP in Google ADK

Prerequisites

Python 3.10+ installed
google-adk package (pip install google-adk)
Active Vinkius subscription with a valid endpoint token

1

Install Google ADK

Run pip install google-adk to install the Agent Development Kit. MCP support is included via the McpToolset class.
2

Connect via SSE transport

Use McpToolset.from_server() with SseServerParams pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create an LlmAgent

Pass the returned mcp_tools list directly to LlmAgent(tools=mcp_tools). The ADK maps each MCP tool to a native Gemini function call — no manual schema definitions required.
4

Run with any Gemini model

The agent works with any Gemini model (gemini-2.0-flash, gemini-2.5-pro, etc.). Copy the full example on the right to get started with Inep Dados Abertos tools in your ADK agent.

agent.py

from google.adk.agents import LlmAgent
from google.adk.tools.mcp_tool.mcp_toolset import McpToolset
from google.adk.tools.mcp_tool.mcp_session_manager import SseServerParams

# Connect to the MCP via SSE
mcp_tools, exit_stack = await McpToolset.from_server(
    connection_params=SseServerParams(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    )
)

# Create your agent with auto-discovered tools
agent = LlmAgent(
    name="Inep Dados Abertos_agent",
    model="gemini-2.0-flash",
    instruction="You have access to Inep Dados Abertos tools via MCP.",
    tools=mcp_tools,
)

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Common questions about Inep Dados Abertos MCP in Google ADK

You initialize `McpToolset` with the Vinkius connection parameters in your Python code. Pass this toolset directly to your `LlmAgent` to expose all 12 educational data tools.

Yes, you can use the optional `tool_names` filter when setting up your `McpToolset`. This prevents Gemini from executing heavy SQL queries if you only want it to list datasets.

While the MCP Server queries the INEP API directly using `search_datastore_sql`, your agent can easily write those results to BigQuery. You write the Python logic to bridge the MCP tool output to your cloud database.

The server supports both Stdio and HTTP transports to handle different deployment environments. For large-scale data, configure your agent to request paginated results from `search_datastore`.

All API requests to INEP are encrypted in transit using standard TLS. Vinkius operates a zero-trust MCP sandbox that executes the server code without keeping logs of the retrieved educational records.

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