INMET (Apitempo - Meteorologia) MCP Server for LlamaIndexGive LlamaIndex instant access to 8 tools to Get All Forecasts, Get Forecast By City, Get Meteorological Data By Date, and more

Name: INMET (Apitempo - Meteorologia)
Price: 29 USD
Availability: InStock
Rating: 5.0 (1 reviews)
Author: Vinkius

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LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add INMET (Apitempo - Meteorologia) as an MCP tool provider through Vinkius and your agents can query, analyze, and act on live data alongside your existing indexes.

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The INMET (Apitempo - Meteorologia) MCP Server for LlamaIndex is a standout in the Government Public Data category — giving your AI agent 8 tools to work with, ready to go from day one.

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python

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

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    mcp_client = BasicMCPClient("https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")
    mcp_tool_spec = McpToolSpec(client=mcp_client)
    tools = await mcp_tool_spec.to_tool_list_async()

    agent = FunctionAgent(
        tools=tools,
        llm=OpenAI(model="gpt-4o"),
        system_prompt=(
            "You are an assistant with access to INMET (Apitempo - Meteorologia). "
            "You have 8 tools available."
        ),
    )

    response = await agent.run(
        "What tools are available in INMET (Apitempo - Meteorologia)?"
    )
    print(response)

asyncio.run(main())

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* Every MCP server runs on Vinkius-managed infrastructure inside AWS - a purpose-built runtime with per-request V8 isolates, Ed25519 signed audit chains, and sub-40ms cold starts optimized for native MCP execution. See our infrastructure

About INMET (Apitempo - Meteorologia) MCP Server

Connect to the INMET (Instituto Nacional de Meteorologia) API to retrieve comprehensive weather data across Brazil. This server allows AI agents to query a vast network of automatic and manual stations, providing precise atmospheric measurements and forecasts.

LlamaIndex agents combine INMET (Apitempo - Meteorologia) tool responses with indexed documents for comprehensive, grounded answers. Connect 8 tools through Vinkius and query live data alongside vector stores and SQL databases in a single turn. ideal for hybrid search, data enrichment, and analytical workflows.

What you can do

Meteorological Stations — List all automatic (T) and manual (M) stations across the Brazilian territory.
Historical & Real-time Data — Fetch daily or hourly measurements (temperature, humidity, pressure) for specific station IDs.
Regional Analysis — Query data for all stations within specific Brazilian regions (N, NE, CO, SE, S) for a given date.
Weather Forecasts — Get detailed forecasts for cities using IBGE codes or retrieve all available forecasts at once.
Satellite Imagery — Access the latest GOES-16 satellite metadata and image URLs for visual weather monitoring.

The INMET (Apitempo - Meteorologia) MCP Server exposes 8 tools through the Vinkius. Connect it to LlamaIndex in under two minutes — credentials fully managed, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

All 8 INMET (Apitempo - Meteorologia) tools available for LlamaIndex

When LlamaIndex connects to INMET (Apitempo - Meteorologia) through Vinkius, your AI agent gets direct access to every tool listed below — spanning meteorology, brazil-weather, weather-forecast, and more. Every call runs in a secure, isolated environment with full audit visibility. Beyond a simple connection, you get real-time monitoring of agent activity, enterprise governance, and optimized token usage.

get

Get all forecasts on INMET (Apitempo - Meteorologia)

Get weather forecasts for all supported cities

get

Get forecast by city on INMET (Apitempo - Meteorologia)

Get weather forecast for a specific city

get

Get meteorological data by date on INMET (Apitempo - Meteorologia)

Get meteorological data by date for a station

get

Get meteorological data by region on INMET (Apitempo - Meteorologia)

Get meteorological data for all stations in a specific region

get

Get satellite images on INMET (Apitempo - Meteorologia)

Get latest GOES-16 satellite images

get

Get station data daily on INMET (Apitempo - Meteorologia)

Get daily meteorological data for a specific station

get

Get station data hourly on INMET (Apitempo - Meteorologia)

Get hourly data for a specific station and time

list

List stations on INMET (Apitempo - Meteorologia)

List meteorological stations by type

Connect INMET (Apitempo - Meteorologia) to LlamaIndex via MCP

Follow these steps to wire INMET (Apitempo - Meteorologia) into LlamaIndex. The entire setup takes under two minutes — your credentials stay safe behind Vinkius.

Install dependencies

Run pip install llama-index-tools-mcp llama-index-llms-openai

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token

Run the agent

Save to agent.py and run: python agent.py

Explore tools

The agent discovers 8 tools from INMET (Apitempo - Meteorologia)

Why Use LlamaIndex with the INMET (Apitempo - Meteorologia) MCP Server

LlamaIndex provides unique advantages when paired with INMET (Apitempo - Meteorologia) through the Model Context Protocol.

Data-first architecture: LlamaIndex agents combine INMET (Apitempo - Meteorologia) tool responses with indexed documents for comprehensive, grounded answers

Query pipeline framework lets you chain INMET (Apitempo - Meteorologia) tool calls with transformations, filters, and re-rankers in a typed pipeline

Multi-source reasoning: agents can query INMET (Apitempo - Meteorologia), a vector store, and a SQL database in a single turn and synthesize results

Observability integrations show exactly what INMET (Apitempo - Meteorologia) tools were called, what data was returned, and how it influenced the final answer

INMET (Apitempo - Meteorologia) + LlamaIndex Use Cases

Practical scenarios where LlamaIndex combined with the INMET (Apitempo - Meteorologia) MCP Server delivers measurable value.

Hybrid search: combine INMET (Apitempo - Meteorologia) real-time data with embedded document indexes for answers that are both current and comprehensive

Data enrichment: query INMET (Apitempo - Meteorologia) to augment indexed data with live information before generating user-facing responses

Knowledge base agents: build agents that maintain and update knowledge bases by periodically querying INMET (Apitempo - Meteorologia) for fresh data

Analytical workflows: chain INMET (Apitempo - Meteorologia) queries with LlamaIndex's data connectors to build multi-source analytical reports

Example Prompts for INMET (Apitempo - Meteorologia) in LlamaIndex

Ready-to-use prompts you can give your LlamaIndex agent to start working with INMET (Apitempo - Meteorologia) immediately.

"List all automatic weather stations in Brazil."

"What is the weather forecast for city code 3304557?"

"Show me the latest satellite images from GOES-16."

Troubleshooting INMET (Apitempo - Meteorologia) MCP Server with LlamaIndex

Common issues when connecting INMET (Apitempo - Meteorologia) to LlamaIndex through Vinkius, and how to resolve them.

BasicMCPClient not found

Install: pip install llama-index-tools-mcp

INMET (Apitempo - Meteorologia) + LlamaIndex FAQ

Common questions about integrating INMET (Apitempo - Meteorologia) MCP Server with LlamaIndex.

How does LlamaIndex connect to MCP servers?

Use the MCP client adapter to create a connection. LlamaIndex discovers all tools and wraps them as query engine tools compatible with any LlamaIndex agent.

Can I combine MCP tools with vector stores?

Yes. LlamaIndex agents can query INMET (Apitempo - Meteorologia) tools and vector store indexes in the same turn, combining real-time and embedded data for grounded responses.