How to Use the Agro MCP in LlamaIndex

Q: How do I configure Agro for LlamaIndex?

Use BasicMCPClient with the Vinkius endpoint URL. Wrap it in McpToolSpec and call totoollistasync() to pass the agricultural functions to your agent.

Q: Can LlamaIndex query Agro satellite imagery metadata?

Yes, the agent executes searchimagery and indexes the returned capture dates and cloud cover percentages. You can then run semantic searches against that metadata.

Index live agricultural data directly into your LlamaIndex RAG applications.

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Connect Agro MCP to LlamaIndex

Create your Vinkius account to connect Agro 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 Agro with LlamaIndex

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Index live soil and weather metrics

Agro exposes tools for gathering field-level climate and terrain data. Your LlamaIndex application calls `get_current_soil` and immediately embeds the resulting moisture and temperature readings into a vector store. Users query the index to find out exactly when a specific field last dried out. Adding historical context takes one more step. The agent runs `get_historical_weather` and stores decades of precipitation data alongside your internal agronomy PDFs. When someone asks about drought conditions, the system pulls from actual API records instead of guessing.

Build a searchable polygon database with MCP

Managing farm boundaries becomes a queryable knowledge base. You define areas using `create_polygon`, and LlamaIndex tracks these spatial definitions in its document store. Retrieving info for a specific plot happens automatically when the agent triggers `get_polygon`. Linking these geometries to imagery searches creates a powerful RAG setup. The `search_imagery` tool finds satellite passes over your indexed polygons. Those metadata results get chunked and indexed, allowing users to ask natural language questions about satellite coverage.

Track crop health indexes over time

Vegetation indexes tell you how well crops are growing. The `get_ndvi_history` tool pulls EVI and NDVI values for your fields, which LlamaIndex then synthesizes into searchable summaries. Agronomists ask the agent to compare last year's yield with the indexed NDVI charts. You control exactly what the agent can access. By applying an allowed tools filter, you restrict the system to only use `get_accumulated_precipitation` and `get_accumulated_temperature`. This keeps the index focused strictly on climate factors.

Setup guide

Set up Agro 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 Agro 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 Agro tools.",
)
response = await agent.run("List recent Agro data")

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Common questions about Agro MCP in LlamaIndex

Use `BasicMCPClient` with the Vinkius endpoint URL. Wrap it in `McpToolSpec` and call `to_tool_list_async()` to pass the agricultural functions to your agent.

Yes, the agent executes `search_imagery` and indexes the returned capture dates and cloud cover percentages. You can then run semantic searches against that metadata.

LlamaIndex embeds the tool outputs into your vector store. This means past weather queries and polygon definitions remain searchable for future sessions without hitting the API again.

The framework supports filtering the available functions. You can expose `get_current_uvi` while blocking `delete_polygon` to prevent accidental data loss.

Vinkius processes your soil moisture and temperature requests inside ephemeral sandboxes. The connection terminates after the payload returns to your client, leaving zero trace of your specific queries on our network.

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