Can my AI check real-time soil moisture and temperature for my farm location?

Yes! Use the `get_latest_soil` tool with your farm latitude and longitude coordinates. The API returns current soil moisture percentage and soil temperature in Celsius. This data is updated in near real-time using satellite and sensor fusion models, making it perfect for daily irrigation decisions and crop health monitoring.

How do I get historical soil moisture trends to analyze drought patterns?

Use the `get_historical_soil` tool with your coordinates and the number of days you want to analyze (e.g., 30, 60, or 90 days). This returns daily soil moisture and temperature readings that you can use to identify drying trends, assess drought severity, and plan irrigation schedules based on historical patterns.

What soil properties are available and how can they help with crop planning?

Use the `get_soil_properties` tool to access detailed soil composition data including sand/silt/clay percentages, organic carbon content, bulk density, pH, and cation exchange capacity (CEC). These properties help determine soil texture classification, drainage capacity, nutrient retention, and optimal crop selection for your land.

Ambee Soil MCP Server for OpenAI Agents SDK 5 tools — connect in under 2 minutes

Name: Ambee Soil
Availability: InStock
Author: Vinkius

openai.github.io/openai-agents-python/mcp

Built by Vinkius GDPR 5 Tools SDK

The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Ambee Soil through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.

Get MCP Server for AI Agents

ASK AI ABOUT THIS MCP SERVER

Open in ChatGPT Open in Claude Open in Perplexity

Vinkius supports streamable HTTP and SSE.

python

import asyncio
from agents import Agent, Runner
from agents.mcp import MCPServerStreamableHttp

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    async with MCPServerStreamableHttp(
        url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    ) as mcp_server:

        agent = Agent(
            name="Ambee Soil Assistant",
            instructions=(
                "You help users interact with Ambee Soil. "
                "You have access to 5 tools."
            ),
            mcp_servers=[mcp_server],
        )

        result = await Runner.run(
            agent, "List all available tools from Ambee Soil"
        )
        print(result.final_output)

asyncio.run(main())

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Stream every event to Splunk, Datadog, or your own webhook in real-time

* 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 Ambee Soil MCP Server

Connect your Ambee Soil API to any AI agent and take full control of real-time soil moisture tracking, temperature monitoring, historical trend analysis, and soil property assessment through natural conversation.

The OpenAI Agents SDK auto-discovers all 5 tools from Ambee Soil through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Ambee Soil, another analyzes results, and a third generates reports, all orchestrated through Vinkius.

What you can do

Real-Time Soil Data — Get current soil moisture and temperature for any global location
Historical Trends — Analyze soil moisture and temperature patterns over past days, weeks, or months
Radius Analysis — Retrieve soil data for multiple points within a specified radius for spatial analysis
Soil Properties — Access detailed soil composition including texture, organic carbon, pH, and bulk density
Grid Mapping — Generate structured gridded soil data for GIS integration and precision agriculture mapping

The Ambee Soil MCP Server exposes 5 tools through the Vinkius. Connect it to OpenAI Agents SDK in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.

How to Connect Ambee Soil to OpenAI Agents SDK via MCP

Follow these steps to integrate the Ambee Soil MCP Server with OpenAI Agents SDK.

Install the SDK

Run pip install openai-agents in your Python environment

Replace the token

Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com

Run the script

Save the code above and run it: python agent.py

Explore tools

The agent will automatically discover 5 tools from Ambee Soil

Why Use OpenAI Agents SDK with the Ambee Soil MCP Server

OpenAI Agents SDK provides unique advantages when paired with Ambee Soil through the Model Context Protocol.

Native MCP integration via `MCPServerSse`, pass the URL and the SDK auto-discovers all tools with full type safety

Built-in guardrails, tracing, and handoff patterns let you build production-grade agents without reinventing safety infrastructure

Lightweight and composable: chain multiple agents and MCP servers in a single pipeline with minimal boilerplate

First-party OpenAI support ensures optimal compatibility with GPT models for tool calling and structured output

Ambee Soil + OpenAI Agents SDK Use Cases

Practical scenarios where OpenAI Agents SDK combined with the Ambee Soil MCP Server delivers measurable value.

Automated workflows: build agents that query Ambee Soil, process the data, and trigger follow-up actions autonomously

Multi-agent orchestration: create specialist agents. one queries Ambee Soil, another analyzes results, a third generates reports

Data enrichment pipelines: stream data through Ambee Soil tools and transform it with OpenAI models in a single async loop

Customer support bots: agents query Ambee Soil to resolve tickets, look up records, and update statuses without human intervention

Ambee Soil MCP Tools for OpenAI Agents SDK (5)

These 5 tools become available when you connect Ambee Soil to OpenAI Agents SDK via MCP:

get_grid_soil

Returns gridded data points suitable for creating soil condition maps, GIS analysis, and spatial interpolation. Essential for precision agriculture mapping, variable rate application planning, and geospatial soil analysis. AI agents should use this when users ask "generate a soil moisture grid for mapping", "get gridded soil data for my field", or need structured spatial soil data for GIS integration. Get soil data on a structured grid for spatial analysis and mapping

get_historical_soil

Essential for analyzing soil condition trends, seasonal patterns, drought assessment, and long-term irrigation planning. AI agents should reference this when users ask "show me soil moisture trends over the past 30 days", "what was the soil temperature last week", or need historical soil data for agricultural analysis. Get historical soil moisture and temperature data for trend analysis

get_latest_soil

Essential for irrigation planning, crop monitoring, soil health assessment, and precision agriculture. AI agents should use this when users ask "what is the soil moisture at my farm", "check current soil temperature", or need immediate soil condition data for agricultural decision making. Get real-time soil moisture and temperature for a specific location

get_soil_by_radius

Returns an array of soil readings across the area, enabling spatial analysis of soil conditions. Essential for regional soil assessment, field variability analysis, and precision agriculture zone mapping. AI agents should use this when users ask "show me soil conditions within 10km of my location", "get soil data for my entire farm area", or need spatial soil moisture distribution analysis. Get soil data for multiple points within a radius of a location

get_soil_properties

Essential for soil classification, crop suitability analysis, fertilizer planning, and long-term soil health monitoring. AI agents should reference this when users ask "what is the soil type and pH at my location", "show me soil organic carbon content", or need comprehensive soil property data for agricultural planning. Get detailed soil physical and chemical properties for a location

Example Prompts for Ambee Soil in OpenAI Agents SDK

Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Ambee Soil immediately.

"What is the current soil moisture and temperature at my farm in Iowa (41.8780, -93.0977)?"

"Show me soil moisture trends over the last 60 days for my location."

"What are the soil properties at my vineyard location? I need to know the pH and organic carbon."

Troubleshooting Ambee Soil MCP Server with OpenAI Agents SDK

Common issues when connecting Ambee Soil to OpenAI Agents SDK through the Vinkius, and how to resolve them.

MCPServerStreamableHttp not found

Ensure you have the latest version: pip install --upgrade openai-agents

Agent not calling tools

Make sure your prompt explicitly references the task the tools can help with.

Ambee Soil + OpenAI Agents SDK FAQ

Common questions about integrating Ambee Soil MCP Server with OpenAI Agents SDK.

How does the OpenAI Agents SDK connect to MCP?

Use MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.