How to Use the INMET (Apitempo - Meteorologia) MCP in LlamaIndex

Index official Brazilian meteorological data into your LlamaIndex vector stores using this MCP Server.

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Connect INMET (Apitempo - Meteorologia) MCP to LlamaIndex

Create your Vinkius account to connect INMET (Apitempo - Meteorologia) 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 INMET (Apitempo - Meteorologia) with LlamaIndex

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Index real-time weather forecasts for RAG

The `get_forecast_by_city` tool retrieves municipal weather forecasts and passes them directly to your indexing pipeline. LlamaIndex converts these text forecasts into vector embeddings, saving them to your local database for semantic search. This lets your agent answer complex queries about regional weather trends without hitting the live INMET API repeatedly. You search past forecasts using natural language instead of writing SQL queries.

Ground your agent in historical station data

The `get_meteorological_data_by_date` tool pulls historical records from specific physical stations. LlamaIndex stores this structured data alongside your documents, creating a unified knowledge base for agricultural or logistics planning. When users ask about climate trends, the agent queries this index first. This minimizes API rate-limiting issues on `get_meteorological_data_by_region` by prioritizing cached, vectorized local data.

Query GOES-16 satellite metadata with LlamaIndex

The `get_satellite_images` tool returns URLs for the latest GOES-16 satellite imagery. LlamaIndex maps these image links to regional metadata, letting your agent retrieve relevant visual feeds during search queries. You can combine this visual metadata with text data from `list_stations` to build a search index. Your agent retrieves both the physical station coordinates and the matching satellite frame in a single query.

Setup guide

Set up INMET (Apitempo - Meteorologia) 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 INMET (Apitempo - Meteorologia) 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 INMET (Apitempo - Meteorologia) tools.",
)
response = await agent.run("List recent INMET (Apitempo - Meteorologia) 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 INMET. 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 INMET (Apitempo - Meteorologia) MCP in LlamaIndex

Use the MCP client to initialize the connection. Wrap it with `McpToolSpec` and call `to_tool_list_async()` to generate the tool array for your agent.

Yes, you can parse the daily station metrics returned by the tool and insert them as document nodes. LlamaIndex will embed and store them for semantic retrieval.

Yes, the tool specification supports async calls natively. This keeps your indexing pipelines running fast when fetching data from multiple stations simultaneously.

Pass an allowed tools filter when creating your tool list. This lets you limit the agent to `get_all_forecasts` while blocking access to historical data endpoints.

All meteorological queries and station IDs are handled within an ephemeral V8 sandbox. Your private vectors and search history stay local to your database, never leaving your infrastructure.

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