GrainSure MCP Server for LlamaIndex 12 tools — connect in under 2 minutes
LlamaIndex specializes in data-aware AI agents that connect LLMs to structured and unstructured sources. Add GrainSure 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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Vinkius supports streamable HTTP and SSE.
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 GrainSure. "
"You have 12 tools available."
),
)
response = await agent.run(
"What tools are available in GrainSure?"
)
print(response)
asyncio.run(main())
* 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 GrainSure MCP Server
Connect your GrainSure Silo Monitoring API to any AI agent and take full control of real-time grain fill level tracking, usage rate analysis, predictive days-to-empty forecasting, and automated delivery management through natural conversation.
LlamaIndex agents combine GrainSure tool responses with indexed documents for comprehensive, grounded answers. Connect 12 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
- Silo Management — List and manage all grain silos with current fill levels, grain types, and monitoring status
- Real-Time Fill Levels — Get current grain fill percentage and remaining tonnes for each silo
- Usage Tracking — Monitor historical grain consumption rates and identify usage trends
- Days to Empty — Get AI-predicted days until each silo runs empty based on current usage patterns
- Fill Level History — Track how fill levels have changed over time for delivery effectiveness analysis
- Low Stock Alerts — Receive automated alerts when silo levels drop below configured thresholds
- Delivery Orders — Create and manage grain delivery orders for timely inventory replenishment
- Order History — Track past deliveries, quantities, and supplier performance
- Sensor Health — Monitor IoT sensor battery levels, signal strength, and calibration status
- Farm Overview — Get comprehensive farm-wide inventory summaries for executive reporting
- Silo Settings — Customize alert thresholds, grain types, and usage rate assumptions
The GrainSure MCP Server exposes 12 tools through the Vinkius. Connect it to LlamaIndex 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 GrainSure to LlamaIndex via MCP
Follow these steps to integrate the GrainSure MCP Server with LlamaIndex.
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 12 tools from GrainSure
Why Use LlamaIndex with the GrainSure MCP Server
LlamaIndex provides unique advantages when paired with GrainSure through the Model Context Protocol.
Data-first architecture: LlamaIndex agents combine GrainSure tool responses with indexed documents for comprehensive, grounded answers
Query pipeline framework lets you chain GrainSure tool calls with transformations, filters, and re-rankers in a typed pipeline
Multi-source reasoning: agents can query GrainSure, a vector store, and a SQL database in a single turn and synthesize results
Observability integrations show exactly what GrainSure tools were called, what data was returned, and how it influenced the final answer
GrainSure + LlamaIndex Use Cases
Practical scenarios where LlamaIndex combined with the GrainSure MCP Server delivers measurable value.
Hybrid search: combine GrainSure real-time data with embedded document indexes for answers that are both current and comprehensive
Data enrichment: query GrainSure 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 GrainSure for fresh data
Analytical workflows: chain GrainSure queries with LlamaIndex's data connectors to build multi-source analytical reports
GrainSure MCP Tools for LlamaIndex (12)
These 12 tools become available when you connect GrainSure to LlamaIndex via MCP:
create_delivery_order
Accepts delivery quantity (tonnes), preferred delivery date, supplier information, and any special instructions. Returns order confirmation with order ID, estimated delivery date, and tracking information. Essential for proactive inventory replenishment, automated ordering based on predictions, and supply chain management. AI agents should use this when users ask "order 20 tonnes of wheat for silo 3", "schedule a delivery for silo 5 next week", or need to place delivery orders based on low stock predictions. Create a new grain delivery order for a specific silo
get_current_level
Returns fill percentage, remaining tonnes, current level height, and last update timestamp. Essential for real-time inventory tracking, delivery planning, and stock management. AI agents should use this when users ask "what is the current fill level in silo 2", "how much grain is left in silo 4", or need immediate stock level data for feed planning and delivery decisions. Get real-time grain fill level for a specific silo
get_days_to_empty
Returns estimated days to empty, predicted empty date, confidence score, and usage rate assumptions. Essential for proactive delivery planning, preventing stock-outs, and optimizing supply chain timing. AI agents should use this when users ask "when will silo 3 run empty", "how many days of feed are left in silo 5", or need predictive supply data for delivery scheduling. Get AI-predicted days until a silo runs empty based on current usage patterns
get_farm_overview
Essential for executive reporting, farm-wide inventory assessment, and strategic supply planning. AI agents should use this when users ask "give me an overview of all my silos", "what is the total grain inventory across the farm", or need farm-level summaries for management reporting. Get comprehensive overview of all monitored silos on the farm
get_fill_level_history
Returns time-series fill percentage data with timestamps showing how stock levels have changed over time. Essential for fill trend analysis, delivery effectiveness assessment, and consumption pattern identification. AI agents should use this when users ask "show me fill level trends for silo 1 over the past 60 days", "has silo 2 been filling or depleting", or need historical fill data for inventory management. Optional days parameter controls lookback period. Get historical fill level readings for a specific silo
get_low_stock_alerts
Returns alert severity (critical, warning, info), affected silo, current fill percentage, threshold level, timestamp, and recommended actions. Essential for proactive inventory management, preventing stock-outs, and timely delivery ordering. AI agents should use this when users ask "show me all low stock alerts", "is silo 3 running low", or need alert data for inventory monitoring. Optional silo_id filters alerts for a specific silo. Get low stock alerts for silos or a specific silo
get_order_history
Essential for delivery tracking, supplier performance assessment, and inventory replenishment planning. AI agents should reference this when users ask "show me delivery history for silo 2", "when was the last delivery to silo 4", or need order data for supply chain analysis. Get delivery order history for a specific silo
get_sensor_health
Returns sensor battery level, signal strength, last communication time, calibration status, and operational status (active, low battery, offline, needs calibration). Essential for sensor maintenance, data continuity assurance, and monitoring system reliability. AI agents should reference this when users ask "is the sensor working in silo 5", "does silo 3 need sensor calibration", or need sensor health data for system administration. Get health status of the level monitoring sensor for a specific silo
get_silo_details
Essential for understanding silo context before analyzing usage data, planning deliveries, or generating inventory reports. AI agents should reference this when users ask "tell me about silo 3", "what grain is stored in silo 5", or need detailed silo metadata for informed decisions. Get detailed information about a specific grain silo
get_silos
Returns silo IDs, names, locations, grain types, current fill levels, and monitoring status. Essential for farm overview, silo inventory management, and selecting specific silos for detailed analysis. AI agents should use this when users ask "show me all my silos", "list monitored storage units", or need to identify available silos before querying fill levels or usage data. List all grain silos monitored by GrainSure
get_usage_history
Returns time-series usage data (tonnes per day/week) with timestamps. Essential for consumption trend analysis, feed rate calculation, and delivery timing optimization. AI agents should reference this when users ask "show me grain usage trends for silo 3", "what is the daily consumption rate for silo 5", or need historical usage data for feed planning and inventory forecasting. Optional days parameter controls lookback period. Get historical grain usage data for a specific silo
update_silo_settings
Essential for customizing monitoring behavior, adjusting alert sensitivity, and maintaining accurate silo profiles. AI agents should use this when users ask "change the low stock threshold for silo 3 to 20 percent", "update silo 5 grain type to barley", or need to modify silo monitoring configuration. Update silo monitoring settings including alert thresholds and grain type
Example Prompts for GrainSure in LlamaIndex
Ready-to-use prompts you can give your LlamaIndex agent to start working with GrainSure immediately.
"Show me the current fill levels for all my silos."
"How many days until my wheat silo runs empty?"
"Order 30 tonnes of barley for silo 2 with delivery next week."
Troubleshooting GrainSure MCP Server with LlamaIndex
Common issues when connecting GrainSure to LlamaIndex through the Vinkius, and how to resolve them.
BasicMCPClient not found
pip install llama-index-tools-mcpGrainSure + LlamaIndex FAQ
Common questions about integrating GrainSure MCP Server with LlamaIndex.
How does LlamaIndex connect to MCP servers?
Can I combine MCP tools with vector stores?
Does LlamaIndex support async MCP calls?
Connect GrainSure with your favorite client
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TypeScript toolkit for building AI-powered web applications.
TypeScript-native agent framework for modern web stacks.
Python framework for orchestrating collaborative AI agent crews.
Leading Python framework for composable LLM applications.
Data-aware AI agent framework for structured and unstructured sources.
Microsoft's framework for multi-agent collaborative conversations.
Connect GrainSure to LlamaIndex
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.