Sentinel Hub MCP Server for AutoGenGive AutoGen instant access to 14 tools to Catalog Search, Check Sentinel Hub Status, Generate False Color Evalscript, and more
Microsoft AutoGen enables multi-agent conversations where agents negotiate, delegate, and execute tasks collaboratively. Add Sentinel Hub as an MCP tool provider through Vinkius and every agent in the group can access live data and take action.
Ask AI about this App Connector for AutoGen
The Sentinel Hub app connector for AutoGen is a standout in the Cloud Infrastructure category — giving your AI agent 14 tools to work with, ready to go from day one.
Vinkius delivers Streamable HTTP and SSE to any MCP client
import asyncio
from autogen_agentchat.agents import AssistantAgent
from autogen_ext.tools.mcp import McpWorkbench
async def main():
# Your Vinkius token. get it at cloud.vinkius.com
async with McpWorkbench(
server_params={"url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"},
transport="streamable_http",
) as workbench:
tools = await workbench.list_tools()
agent = AssistantAgent(
name="sentinel_hub_agent",
tools=tools,
system_message=(
"You help users with Sentinel Hub. "
"14 tools available."
),
)
print(f"Agent ready with {len(tools)} tools")
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 Sentinel Hub MCP Server
Connect to Sentinel Hub — the most powerful satellite imagery processing API in Europe — and transform raw Earth observation data into actionable intelligence.
AutoGen enables multi-agent conversations where agents negotiate, delegate, and collaboratively use Sentinel Hub tools. Connect 14 tools through Vinkius and assign role-based access. a data analyst queries while a reviewer validates, with optional human-in-the-loop approval for sensitive operations.
What you can do
- STAC Catalog Search — Discover available satellite scenes by location, date, collection, cloud cover, and MGRS tile ID across all Sentinel missions and Landsat
- Image Processing — Render custom satellite imagery using evalscripts (JavaScript-based processing scripts) that define how bands are combined, indices are calculated, and pixels are colored
- Vegetation Analysis (NDVI) — Generate ready-to-use NDVI evalscripts that color-code vegetation density from bare soil to dense forest
- Statistical Analysis — Calculate mean, min, max, standard deviation, and histograms over areas of interest with temporal aggregation (daily, weekly, monthly)
- Cloud-Free Search — Find satellite scenes below a specified cloud cover threshold for clean optical analysis
- Band Combinations — Access a curated library of 10 predefined band combinations including True Color, False Color, NDWI, Moisture Index, SWIR, and Burn Severity
The Sentinel Hub MCP Server exposes 14 tools through the Vinkius. Connect it to AutoGen in under two minutes — no API keys to rotate, no infrastructure to provision, no vendor lock-in. Your configuration, your data, your control.
All 14 Sentinel Hub tools available for AutoGen
When AutoGen connects to Sentinel Hub through Vinkius, your AI agent gets direct access to every tool listed below — spanning satellite-imagery, earth-observation, geospatial-data, and more. Every call is secured with network, filesystem, subprocess, and code evaluation entitlements inside a sandboxed runtime. Beyond a simple connection, you get a full AI Gateway with real-time visibility into agent activity, enterprise governance, and optimized token usage.
Specify a collection ID (e.g., "sentinel-2-l2a", "sentinel-1-grd"), a bounding box as [west, south, east, north] coordinates, and a date range. Returns item metadata including geometry, cloud cover, and band information. Search the Sentinel Hub STAC catalog for satellite imagery
Returns the connection status and service URL. Use this to verify your client_id:client_secret credentials are working correctly. Verify Sentinel Hub API connectivity and authentication
In the output, healthy vegetation appears bright red, urban areas appear cyan/grey, and water appears dark blue. This is the standard false-color composite used in remote sensing for vegetation mapping and land cover classification. Generate a false-color evalscript for vegetation emphasis
The output is color-coded: dark for water/shadow, grey for bare soil, yellow-green for sparse vegetation, and deep green for dense vegetation. Use the returned evalscript with the process_image tool. Generate a ready-to-use NDVI evalscript for vegetation analysis
Use the returned evalscript with the process_image tool to get visually appealing satellite photos of any location on Earth. Generate a true-color RGB evalscript for natural imagery
Get detailed information about a specific data collection
Use the item ID returned from a catalog_search query. Get detailed metadata for a specific STAC catalog item
Requires an evalscript that defines which bands to analyze. Supports temporal aggregation (daily, weekly, monthly) for time-series analysis of vegetation indices, water levels, or urban expansion. Calculate statistics over an area from satellite imagery
Useful for verifying credentials and understanding available quotas. Get authenticated Sentinel Hub user profile information
Includes True Color, False Color (vegetation), NDVI, NDWI, Moisture Index, SWIR, SAR polarizations, Scene Classification, and Burn Severity (NBR). Each entry specifies the required bands and target collection. List predefined satellite band combinations and indices
Includes Sentinel-1 GRD (radar), Sentinel-2 L1C/L2A (optical), Sentinel-3 OLCI/SLSTR, Sentinel-5P (atmosphere), Landsat 8-9, DEM, and Copernicus Land Monitoring Service data. List all available Sentinel Hub satellite data collections
Specify the data collection, area of interest as a bounding box, date range, and the evalscript. The evalscript defines band inputs, processing logic, and output format. Use generate_ndvi_evalscript or generate_true_color_evalscript tools to get ready-made evalscripts. Process satellite imagery with a custom evalscript
MGRS tiles are the standard spatial reference for Sentinel-2 data (e.g., "33UUP" for central Europe, "29SQB" for Lisbon area). Returns all scenes for the specified tile within the date range. Search Sentinel-2 imagery by MGRS tile identifier
Essential for optical analysis where cloud contamination would corrupt results. Typical thresholds: <10% for clean analysis, <30% for general use, <50% for temporal coverage. Search for cloud-free satellite imagery below a threshold
Connect Sentinel Hub to AutoGen via MCP
Follow these steps to wire Sentinel Hub into AutoGen. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.
Install AutoGen
pip install "autogen-ext[mcp]"Replace the token
[YOUR_TOKEN_HERE] with your Vinkius tokenIntegrate into workflow
Explore tools
Why Use AutoGen with the Sentinel Hub MCP Server
AutoGen provides unique advantages when paired with Sentinel Hub through the Model Context Protocol.
Multi-agent conversations: multiple AutoGen agents discuss, delegate, and collaboratively use Sentinel Hub tools to solve complex tasks
Role-based architecture lets you assign Sentinel Hub tool access to specific agents. a data analyst queries while a reviewer validates
Human-in-the-loop support: agents can pause for human approval before executing sensitive Sentinel Hub tool calls
Code execution sandbox: AutoGen agents can write and run code that processes Sentinel Hub tool responses in an isolated environment
Sentinel Hub + AutoGen Use Cases
Practical scenarios where AutoGen combined with the Sentinel Hub MCP Server delivers measurable value.
Collaborative analysis: one agent queries Sentinel Hub while another validates results and a third generates the final report
Automated review pipelines: a researcher agent fetches data from Sentinel Hub, a critic agent evaluates quality, and a writer produces the output
Interactive planning: agents negotiate task allocation using Sentinel Hub data to make informed decisions about resource distribution
Code generation with live data: an AutoGen coder agent writes scripts that process Sentinel Hub responses in a sandboxed execution environment
Example Prompts for Sentinel Hub in AutoGen
Ready-to-use prompts you can give your AutoGen agent to start working with Sentinel Hub immediately.
"Show me an NDVI vegetation analysis for the Amazon rainforest region."
"Find cloud-free Sentinel-2 imagery over Paris with less than 10% clouds."
"What band combinations can I use for wildfire assessment?"
Troubleshooting Sentinel Hub MCP Server with AutoGen
Common issues when connecting Sentinel Hub to AutoGen through the Vinkius, and how to resolve them.
McpWorkbench not found
pip install "autogen-ext[mcp]"Sentinel Hub + AutoGen FAQ
Common questions about integrating Sentinel Hub MCP Server with AutoGen.