Wiagro MCP Server for CrewAI 12 tools — connect in under 2 minutes
Connect your CrewAI agents to Wiagro through Vinkius, pass the Edge URL in the `mcps` parameter and every Wiagro tool is auto-discovered at runtime. No credentials to manage, no infrastructure to maintain.
ASK AI ABOUT THIS MCP SERVER
Vinkius supports streamable HTTP and SSE.
from crewai import Agent, Task, Crew
agent = Agent(
role="Wiagro Specialist",
goal="Help users interact with Wiagro effectively",
backstory=(
"You are an expert at leveraging Wiagro tools "
"for automation and data analysis."
),
# Your Vinkius token. get it at cloud.vinkius.com
mcps=["https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"],
)
task = Task(
description=(
"Explore all available tools in Wiagro "
"and summarize their capabilities."
),
agent=agent,
expected_output=(
"A detailed summary of 12 available tools "
"and what they can do."
),
)
crew = Crew(agents=[agent], tasks=[task])
result = crew.kickoff()
print(result)
* 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 Wiagro MCP Server
Connect your Wiagro Smart Silobag API to any AI agent and take full control of IoT-based grain condition monitoring, rupture detection, satellite environmental monitoring, and silobag quality management through natural conversation.
When paired with CrewAI, Wiagro becomes a first-class tool in your multi-agent workflows. Each agent in the crew can call Wiagro tools autonomously, one agent queries data, another analyzes results, a third compiles reports, all orchestrated through Vinkius with zero configuration overhead.
What you can do
- Silobag Management — List and manage all silobags and conventional silos with grain types, fill levels, and monitoring status
- Real-Time Readings — Get current temperature, intergranular humidity, and CO2 readings from IoT sensors throughout the grain mass
- Temperature History — Track historical temperature trends to detect hot spots and spoilage heating
- Humidity History — Monitor intergranular humidity patterns for moisture migration and condensation detection
- CO2 Tracking — Follow CO2 trends as the earliest indicator of biological activity and grain spoilage
- Rupture Detection — Receive satellite-based alerts for silobag tears, holes, and structural damage
- Alert Management — Monitor active alerts for high temperature, humidity, and CO2 threshold breaches
- Sensor Health — Track IoT sensor battery levels, signal strength, and operational status
- Satellite Monitoring — Access satellite-based environmental data affecting silobag conditions
- Quality Assessment — Get AI-powered grain quality scores with storage life predictions
- Facility Overview — Get comprehensive facility-wide summaries for executive reporting
The Wiagro MCP Server exposes 12 tools through the Vinkius. Connect it to CrewAI 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 Wiagro to CrewAI via MCP
Follow these steps to integrate the Wiagro MCP Server with CrewAI.
Install CrewAI
Run pip install crewai
Replace the token
Replace [YOUR_TOKEN_HERE] with your Vinkius token from cloud.vinkius.com
Customize the agent
Adjust the role, goal, and backstory to fit your use case
Run the crew
Run python crew.py. CrewAI auto-discovers 12 tools from Wiagro
Why Use CrewAI with the Wiagro MCP Server
CrewAI Multi-Agent Orchestration Framework provides unique advantages when paired with Wiagro through the Model Context Protocol.
Multi-agent collaboration lets you decompose complex workflows into specialized roles, one agent researches, another analyzes, a third generates reports, each with access to MCP tools
CrewAI's native MCP integration requires zero adapter code: pass Vinkius Edge URL directly in the `mcps` parameter and agents auto-discover every available tool at runtime
Built-in task delegation and shared memory mean agents can pass context between steps without manual state management, enabling multi-hop reasoning across tool calls
Sequential and hierarchical crew patterns map naturally to real-world workflows: enumerate subdomains → analyze DNS history → check WHOIS records → compile findings into actionable reports
Wiagro + CrewAI Use Cases
Practical scenarios where CrewAI combined with the Wiagro MCP Server delivers measurable value.
Automated multi-step research: a reconnaissance agent queries Wiagro for raw data, then a second analyst agent cross-references findings and flags anomalies. all without human handoff
Scheduled intelligence reports: set up a crew that periodically queries Wiagro, analyzes trends over time, and generates executive briefings in markdown or PDF format
Multi-source enrichment pipelines: chain Wiagro tools with other MCP servers in the same crew, letting agents correlate data across multiple providers in a single workflow
Compliance and audit automation: a compliance agent queries Wiagro against predefined policy rules, generates deviation reports, and routes findings to the appropriate team
Wiagro MCP Tools for CrewAI (12)
These 12 tools become available when you connect Wiagro to CrewAI via MCP:
get_alerts
Returns alert severity (critical, warning, info), alert type, affected silobag, timestamp, and recommended actions. Essential for proactive grain management, quality issue detection, and operational response. AI agents should use this when users ask "show me all active alerts", "what warnings have been triggered for silobag 3", or need alert data for operational monitoring. Optional silobag_id filters alerts for a specific silobag. Get active temperature, humidity, and CO2 alerts for silobags
get_co2_history
CO2 is the earliest indicator of biological activity (mold, insects, grain respiration) that leads to spoilage. Returns time-series CO2 data in ppm with timestamps. Essential for spoilage trend analysis, early warning detection, and validating storage condition stability. AI agents should reference this when users ask "show me CO2 trends for silobag 3 over the past 30 days", "has CO2 been rising in silobag 5", or need historical CO2 data for grain quality assessment. Get historical CO2 readings to track biological activity and spoilage trends
get_current_readings
Returns temperature (Celsius), intergranular humidity (%), and CO2 levels (ppm) from multiple sensor positions throughout the grain mass. Essential for real-time grain quality monitoring, early spoilage detection, and storage condition assessment. AI agents should use this when users ask "what are the current conditions in silobag 2", "show me all sensor readings for silobag 4", or need immediate grain quality data for storage management decisions. Get current temperature, humidity, and CO2 readings from sensors in a silobag
get_facility_overview
Essential for executive reporting, facility-wide quality assessment, and strategic storage management. AI agents should use this when users ask "give me an overview of my entire facility", "what is the overall grain quality status", or need facility-level summaries for management reporting. Get comprehensive overview of all monitored silobags and storage units
get_humidity_history
Humidity migration and condensation are key drivers of spoilage and quality loss. Returns time-series humidity data (%) with timestamps from multiple sensor positions. Essential for moisture migration analysis, condensation detection, and storage safety monitoring. AI agents should use this when users ask "show me humidity trends for silobag 1", "has humidity been stable in silobag 2", or need historical humidity data for storage management. Get historical intergranular humidity readings for moisture migration analysis
get_quality_assessment
Returns quality score, risk level, estimated remaining storage life, and recommended actions. Essential for grain quality monitoring, marketing timing decisions, and storage duration optimization. AI agents should reference this when users ask "what is the grain quality in silobag 3", "assess storage conditions for silobag 5", or need quality assessment data for storage management and marketing decisions. Get AI-powered grain quality assessment for a specific silobag
get_rupture_alerts
Rupture alerts indicate tears, holes, or structural damage to silobags that could expose grain to weather, pests, and spoilage. Returns alert severity, location of rupture, detection timestamp, and recommended actions. Essential for silobag integrity monitoring, grain protection, and preventing quality loss. AI agents should use this when users ask "are there any silobag ruptures detected", "show rupture alerts for silobag 3", or need structural integrity alerts for silobag management. Optional silobag_id filters alerts for a specific silobag. Get silobag rupture detection alerts for all silobags or a specific one
get_satellite_data
Essential for understanding external risk factors, weather impact assessment, and proactive silobag protection. AI agents should use this when users ask "what is the satellite data for silobag 2", "show external conditions affecting silobag 4", or need environmental context for silobag management decisions. Get satellite-based monitoring data for external silobag conditions
get_sensor_health
Returns sensor IDs, positions (depth/location), battery levels, signal strength, last communication time, and operational status (active, low battery, offline). Essential for sensor network maintenance, data continuity assurance, and monitoring system reliability. AI agents should reference this when users ask "are all sensors working in silobag 5", "which sensors need battery replacement", or need sensor network health data for system administration. Get health status of IoT sensors deployed in a silobag
get_silobag_details
Essential for understanding silobag context before analyzing sensor data, planning aeration strategies, or generating quality reports. AI agents should reference this when users ask "tell me about silobag 3", "what grain is stored in silobag 5", or need detailed silobag metadata for informed analysis. Get detailed information about a specific silobag or conventional silo
get_silobags
Returns silobag IDs, names, locations, grain types, fill levels, and current monitoring status. Essential for facility overview, silobag inventory management, and selecting specific silobags for detailed analysis. AI agents should use this when users ask "show me all my silobags", "list monitored storage units", or need to identify available silobags before querying sensor readings or alerts. List all silobags and conventional silos monitored by Wiagro
get_temperature_history
Temperature increases often indicate active spoilage, insect activity, or mold growth. Returns time-series temperature data (Celsius) with timestamps from multiple sensor depths and positions. Essential for hot spot detection, spoilage heating identification, and grain quality preservation. AI agents should reference this when users ask "show me temperature trends for silobag 4", "are there any hot spots developing in silobag 6", or need historical temperature data for spoilage analysis. Optional days parameter controls lookback period. Get historical temperature readings to detect hot spots and spoilage heating in a silobag
Example Prompts for Wiagro in CrewAI
Ready-to-use prompts you can give your CrewAI agent to start working with Wiagro immediately.
"Show me the current temperature, humidity, and CO2 readings for silobag 3."
"Check for any silobag rupture alerts or active warnings across my facility."
"Give me a quality assessment for all my monitored silobags."
Troubleshooting Wiagro MCP Server with CrewAI
Common issues when connecting Wiagro to CrewAI through the Vinkius, and how to resolve them.
MCP tools not discovered
Agent not using tools
Timeout errors
Rate limiting or 429 errors
Wiagro + CrewAI FAQ
Common questions about integrating Wiagro MCP Server with CrewAI.
How does CrewAI discover and connect to MCP tools?
tools/list method. This means tools are always fresh and reflect the server's current capabilities. No tool schemas need to be hardcoded.Can different agents in the same crew use different MCP servers?
mcps list, so you can assign specific servers to specific roles. For example, a reconnaissance agent might use a domain intelligence server while an analysis agent uses a vulnerability database server.What happens when an MCP tool call fails during a crew run?
Can CrewAI agents call multiple MCP tools in parallel?
process=Process.parallel, each calling different MCP tools concurrently. This is ideal for workflows where separate data sources need to be queried simultaneously.Can I run CrewAI crews on a schedule (cron)?
crew.kickoff() method runs synchronously by default, making it straightforward to integrate into existing pipelines.Connect Wiagro with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
Anthropic's native desktop app for Claude with built-in MCP support.
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GitHub Copilot in VS Code with Agent mode and MCP support.
Purpose-built IDE for agentic AI coding workflows.
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Anthropic's agentic CLI for terminal-first development.
Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
Type-safe agent development for Python with first-class MCP support.
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 Wiagro to CrewAI
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.