bruno-portfolio

bruno-portfolio / agrobr-mcp

Public

MCP server for Brazilian agricultural data — connect LLMs to 10 public data sources via agrobr

19
3
100% credibility
Found Feb 17, 2026 at 12 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

MCP server providing AI models with real-time Brazilian agricultural data on prices, crop estimates, climate, deforestation, and more from public sources.

How It Works

1
🌱 Discover Ag Data Boost

You learn about a handy tool that lets your AI assistant pull in fresh Brazilian farm info like prices and weather.

2
📦 Add the Helper

You easily download the farm data add-on to your computer with one quick step.

3
🔗 Link to Your AI Chat

You connect it to your AI app like Claude or Cursor by adding a simple note in settings, and it's ready.

4
💭 Ask Everyday Questions

You chat naturally with your AI: 'soybean prices?', 'crop estimates by state?', or 'Amazon deforestation?'.

5
📊 Get Instant Insights

Your AI shows clear tables with the latest real-time data on prices, harvests, climate, and more.

🥳 Farm-Smart Decisions

Now you stay updated on agriculture effortlessly, helping with better planning and choices.

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Star Growth

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AI-Generated Review

What is agrobr-mcp?

agrobr-mcp is a Python MCP server that lets LLMs query real-time Brazilian agricultural data from 10 public sources like CEPEA, CONAB, and INPE—think crop prices, production estimates, climate stats, and deforestation alerts. Pip-install it and hook it into Claude Desktop, Cursor, or Claude Code with a simple config, turning natural language queries like "soybean prices last 5 days" into formatted results. Built on the MCP protocol and agrobr library, it solves the hassle of scraping scattered ag APIs for AI agents.

Why is it gaining traction?

In the MCP GitHub server Python space, it stands out with ready-to-use tools for spot prices, futures on B3, crop progress by state, and health checks on data sources—perfect for mcp server examples or tutorials. Devs dig the meta tool to list valid products first, avoiding guesswork, and seamless setup in mcp GitHub Copilot VSCode or IntelliJ setups. Low overhead as a thin MCP server AI layer means quick wins over raw API wrangling.

Who should use this?

Agri-tech devs building LLM-powered dashboards for Brazilian commodities traders. Data analysts in Cursor or Claude querying safra estimates or clima by UF without custom scrapers. Project managers tracking mcp GitHub issues for n8n workflows or registry integrations focused on agricultural insights.

Verdict

Grab it if you're in Brazilian ag and need fast MCP server GitHub integration—docs and setup are solid for alpha. With 11 stars and 1.0% credibility score, it's early but testable; watch GitHub issues for polish before production.

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