Taranis MCP Server for OpenAI Agents SDK 12 tools — connect in under 2 minutes
The OpenAI Agents SDK enables production-grade agent workflows in Python. Connect Taranis through Vinkius and your agents gain typed, auto-discovered tools with built-in guardrails. no manual schema definitions required.
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Vinkius supports streamable HTTP and SSE.
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="Taranis Assistant",
instructions=(
"You help users interact with Taranis. "
"You have access to 12 tools."
),
mcp_servers=[mcp_server],
)
result = await Runner.run(
agent, "List all available tools from Taranis"
)
print(result.final_output)
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 Taranis MCP Server
Connect your Taranis AI Scouting API to any AI agent and take full control of AI-powered crop threat detection, ultra-high-resolution imagery analysis, field scouting recommendations, and precision agriculture decision-making through natural conversation.
The OpenAI Agents SDK auto-discovers all 12 tools from Taranis through native MCP integration. Build agents with built-in guardrails, tracing, and handoff patterns. chain multiple agents where one queries Taranis, another analyzes results, and a third generates reports, all orchestrated through Vinkius.
What you can do
- Organizations — List all agricultural organizations and farms in your Taranis account
- Field Management — View all monitored fields with crop types, boundaries, and growth stages
- Flight History — Review all drone and aircraft flight missions with imagery acquisition dates
- Flight Imagery — Access ultra-high-resolution orthomosaics, DSMs, and NDVI maps from each flight
- All Detections — Get comprehensive AI-detected threats (weeds, diseases, pests, nutrients) in any field
- Threat Summary — View consolidated threat severity assessments and trend analysis per field
- Scouting Recommendations — Receive AI-powered action plans for targeted field scouting missions
- Multispectral Analysis — Access NDVI, NDRE, and GNDVI vegetation indices for vigor assessment
- Weed Detection — Identify specific weed species with coverage estimates and herbicide recommendations
- Disease Detection — Detect crop diseases with severity levels and fungicide treatment suggestions
- Nutrient Analysis — Identify nutrient deficiencies with variable rate fertilization recommendations
The Taranis MCP Server exposes 12 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 Taranis to OpenAI Agents SDK via MCP
Follow these steps to integrate the Taranis 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 12 tools from Taranis
Why Use OpenAI Agents SDK with the Taranis MCP Server
OpenAI Agents SDK provides unique advantages when paired with Taranis 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
Taranis + OpenAI Agents SDK Use Cases
Practical scenarios where OpenAI Agents SDK combined with the Taranis MCP Server delivers measurable value.
Automated workflows: build agents that query Taranis, process the data, and trigger follow-up actions autonomously
Multi-agent orchestration: create specialist agents. one queries Taranis, another analyzes results, a third generates reports
Data enrichment pipelines: stream data through Taranis tools and transform it with OpenAI models in a single async loop
Customer support bots: agents query Taranis to resolve tickets, look up records, and update statuses without human intervention
Taranis MCP Tools for OpenAI Agents SDK (12)
These 12 tools become available when you connect Taranis to OpenAI Agents SDK via MCP:
get_detections
Returns detection locations (GPS coordinates), threat types (weeds, diseases, pests, nutrient deficiencies), severity levels, confidence scores, affected area estimates, and recommended actions. Detections are classified by AI models trained on millions of field images for sub-millimeter accuracy. Essential for early threat identification, targeted scouting, and precision treatment planning. AI agents should use this when users ask "show me all detections in my field", "what threats were detected in field X", or need comprehensive threat analysis before planning field operations. Optional threatType filters detections by specific threat category. Get all AI-detected crop threats (weeds, diseases, pests, nutrient deficiencies) in a field
get_disease_detections
Returns disease locations, pathogen identification where possible, severity levels (early, moderate, advanced), affected plant parts, and recommended fungicide treatments. Essential for early disease intervention, fungicide planning, and yield loss prevention. AI agents should reference this when users ask "what diseases are in my soybean field", "show disease progression over time", or need disease-specific analysis for crop protection decisions. Get crop disease detections and severity assessments for a field
get_field_details
Essential for understanding field context before analyzing detections, planning scouting missions, or generating management recommendations. AI agents should reference this when users ask "tell me about this field", "what crop is planted in field X", or need detailed field metadata for context-aware analysis. Get detailed information about a specific agricultural field
get_fields
Returns field names, IDs, boundaries (GeoJSON polygons), area in hectares/acres, crop type, planting dates, and monitoring status. Essential for farm management overview, field inventory, and selecting target fields for threat detection and scouting analysis. AI agents should use this when users ask "show me all fields in my organization", "list monitored fields", or need to identify available fields for detection or flight queries. Optional orgId filters fields by specific organization. List all agricultural fields monitored by Taranis for an organization
get_flight_imagery
Returns orthomosaic mosaics, digital surface models (DSM), digital terrain models (DTM), normalized difference vegetation index (NDVI) maps, and true-color RGB composites. Essential for visual crop assessment, change detection between flights, and downloading high-resolution imagery for GIS analysis. AI agents should reference this when users ask "show me the latest imagery from this flight", "get the NDVI map for flight X", or need specific imagery products for field analysis. Get ultra-high-resolution imagery products from a specific flight mission
get_flights
Returns flight dates, times, aircraft type, imagery resolution, weather conditions during flight, coverage percentage, and processing status. Essential for understanding imagery acquisition history, assessing data quality, and selecting specific flights for detailed analysis. AI agents should use this when users ask "show me all flights over my corn field", "what imagery was captured last week", or need flight metadata before accessing specific imagery products. List all drone or aircraft flights that captured imagery for a specific field
get_multispectral_imagery
Supports indices including NDVI (Normalized Difference Vegetation Index), NDRE (Normalized Difference Red Edge), GNDVI (Green NDVI), and custom band combinations. Returns imagery layers, statistical summaries (mean, min, max, std), and zone classifications. Essential for crop vigor assessment, variable rate application planning, and growth stage monitoring. AI agents should reference this when users ask "show me NDVI map for my field", "get multispectral analysis", or need vegetation index data for precision agriculture planning. Get multispectral imagery and vegetation indices (NDVI, NDRE, GNDVI) for a field
get_nutrient_detections
Returns deficiency locations, severity estimates, affected growth stages, and variable rate fertilization recommendations. Essential for precision nutrient management, yield optimization, and cost-efficient fertilization planning. AI agents should use this when users ask "does my field have nutrient deficiencies", "where do I need to apply nitrogen", or need nutrient-specific analysis for variable rate application planning. Get nutrient deficiency detections and fertilization recommendations for a field
get_organizations
Returns organization names, IDs, contact information, and field counts. Essential for multi-account management, selecting target organizations for field analysis, and understanding the scope of monitored agricultural operations. AI agents should use this when users ask "show me all my organizations", "list farms I have access to", or need to identify available organizations before querying fields or detections. List all organizations available to the user in Taranis platform
get_scouting_recommendations
Returns specific action items including ground truth verification locations, recommended scouting patterns, treatment suggestions, timing recommendations, and priority levels. Essential for field team coordination, targeted scouting missions, and data-driven treatment decisions. AI agents should use this when users ask "what should I scout for in my field this week", "give me scouting recommendations", or need AI-generated action plans based on latest imagery analysis. Get AI-powered scouting recommendations and action plans for a field
get_threats
Returns threat categories, overall severity ratings (low, medium, high, critical), affected area percentages, trend analysis (increasing, stable, decreasing), and priority rankings. Essential for quick field health assessment, prioritizing scouting missions, and making informed treatment decisions. AI agents should reference this when users ask "what is the overall threat level in my field", "summarize field health status", or need a high-level threat overview before diving into individual detections. Get consolidated threat summary and severity assessment for a field
get_weed_detections
Returns weed locations, estimated coverage area, species classification, growth stage, and herbicide resistance indicators. Essential for targeted spot spraying, herbicide selection, and resistance management. AI agents should use this when users ask "where are the weeds in my field", "what weed species were detected", or need weed-specific analysis for precision herbicide application. Get specific weed species detections and infestation maps for a field
Example Prompts for Taranis in OpenAI Agents SDK
Ready-to-use prompts you can give your OpenAI Agents SDK agent to start working with Taranis immediately.
"Show me all AI-detected threats in my corn field from the latest flight."
"Generate scouting recommendations for my soybean field this week."
"What is the overall threat level and NDVI trend for my wheat field this season?"
Troubleshooting Taranis MCP Server with OpenAI Agents SDK
Common issues when connecting Taranis to OpenAI Agents SDK through the Vinkius, and how to resolve them.
MCPServerStreamableHttp not found
pip install --upgrade openai-agentsAgent not calling tools
Taranis + OpenAI Agents SDK FAQ
Common questions about integrating Taranis MCP Server with OpenAI Agents SDK.
How does the OpenAI Agents SDK connect to MCP?
MCPServerSse(url=...) to create a server connection. The SDK auto-discovers all tools and makes them available to your agent with full type information.Can I use multiple MCP servers in one agent?
MCPServerSse instances to the agent constructor. The agent can use tools from all connected servers within a single run.Does the SDK support streaming responses?
Connect Taranis with your favorite client
Step-by-step setup guides for every MCP-compatible client and framework:
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Python SDK for building production-grade OpenAI agent workflows.
Google's framework for building production AI agents.
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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 Taranis to OpenAI Agents SDK
Get your token, paste the configuration, and start using 12 tools in under 2 minutes. No API key management needed.