What is an evalscript and how do I use one?

An evalscript is a small JavaScript program that tells Sentinel Hub how to process satellite bands into an output image. It defines which bands to use, how to combine them, and what colors to assign. You can use the generate_ndvi_evalscript or generate_true_color_evalscript tools to get ready-made evalscripts, then pass them to the process_image tool.

Can I analyze vegetation health with this server?

Absolutely. Generate an NDVI evalscript with the generate_ndvi_evalscript tool, then process imagery for your area of interest with the process_image tool. For time-series analysis, use the get_statistics tool with temporal aggregation to track vegetation changes over weeks or months. The search_cloud_free tool helps you find clean scenes without cloud contamination.

What is the difference between this server and the Copernicus Data Space server?

The Copernicus Data Space server focuses on product catalogue search and download — finding and retrieving raw satellite data files. Sentinel Hub focuses on on-the-fly processing — rendering images, computing indices, and generating statistics without downloading raw data. They complement each other: use Copernicus for data discovery and bulk download, Sentinel Hub for real-time analysis and visualization.

INSTANT PLUG AND PLAY

Sentinel Hub MCP Server for Pydantic AIGive Pydantic AI instant access to 14 tools to Catalog Search, Check Sentinel Hub Status, Generate False Color Evalscript, and more

Name: Sentinel Hub
Availability: InStock
Author: Vinkius

Built by Vinkius GDPR 14 Tools SDK

Pydantic AI brings type-safe agent development to Python with first-class MCP support. Connect Sentinel Hub through Vinkius and every tool is automatically validated against Pydantic schemas. catch errors at build time, not in production.

Use with Your AI Agent

Ask AI about this App Connector for Pydantic AI

Open in ChatGPT Open in Claude Open in Perplexity

The Sentinel Hub app connector for Pydantic AI 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

python

import asyncio
from pydantic_ai import Agent
from pydantic_ai.mcp import MCPServerHTTP

async def main():
    # Your Vinkius token. get it at cloud.vinkius.com
    server = MCPServerHTTP(url="https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp")

    agent = Agent(
        model="openai:gpt-4o",
        mcp_servers=[server],
        system_prompt=(
            "You are an assistant with access to Sentinel Hub "
            "(14 tools)."
        ),
    )

    result = await agent.run(
        "What tools are available in Sentinel Hub?"
    )
    print(result.data)

asyncio.run(main())

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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.

Pydantic AI validates every Sentinel Hub tool response against typed schemas, catching data inconsistencies at build time. Connect 14 tools through Vinkius and switch between OpenAI, Anthropic, or Gemini without changing your integration code. full type safety, structured output guarantees, and dependency injection for testable agents.

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 Pydantic AI 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 Pydantic AI

When Pydantic AI 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.

catalog_search

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

check_sentinel_hub_status

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

generate_false_color_evalscript

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

generate_ndvi_evalscript

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

generate_true_color_evalscript

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_catalog_collection

Get detailed information about a specific data collection

get_catalog_item

Use the item ID returned from a catalog_search query. Get detailed metadata for a specific STAC catalog item

get_statistics

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

get_user_info

Useful for verifying credentials and understanding available quotas. Get authenticated Sentinel Hub user profile information

list_band_combinations

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

list_catalog_collections

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

process_image

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

search_by_tile

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

search_cloud_free

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 Pydantic AI via MCP

Follow these steps to wire Sentinel Hub into Pydantic AI. The entire setup takes under two minutes — your credentials stay safe behind the Vinkius.

Install Pydantic AI

Run pip install pydantic-ai

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 14 tools from Sentinel Hub with type-safe schemas

Why Use Pydantic AI with the Sentinel Hub MCP Server

Pydantic AI provides unique advantages when paired with Sentinel Hub through the Model Context Protocol.

Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application

Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your Sentinel Hub integration code

Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors

Dependency injection system cleanly separates your Sentinel Hub connection logic from agent behavior for testable, maintainable code

Sentinel Hub + Pydantic AI Use Cases

Practical scenarios where Pydantic AI combined with the Sentinel Hub MCP Server delivers measurable value.

Type-safe data pipelines: query Sentinel Hub with guaranteed response schemas, feeding validated data into downstream processing

API orchestration: chain multiple Sentinel Hub tool calls with Pydantic validation at each step to ensure data integrity end-to-end

Production monitoring: build validated alert agents that query Sentinel Hub and output structured, schema-compliant notifications

Testing and QA: use Pydantic AI's dependency injection to mock Sentinel Hub responses and write comprehensive agent tests

Example Prompts for Sentinel Hub in Pydantic AI

Ready-to-use prompts you can give your Pydantic AI 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 Pydantic AI

Common issues when connecting Sentinel Hub to Pydantic AI through the Vinkius, and how to resolve them.

MCPServerHTTP not found

Update: pip install --upgrade pydantic-ai

Sentinel Hub + Pydantic AI FAQ

Common questions about integrating Sentinel Hub MCP Server with Pydantic AI.

How does Pydantic AI discover MCP tools?

Create an MCPServerHTTP instance with the server URL. Pydantic AI connects, discovers all tools, and generates typed Python interfaces automatically.

Does Pydantic AI validate MCP tool responses?

Yes. When you define result types as Pydantic models, every tool response is validated against the schema. Invalid data raises a clear error instead of silently corrupting your pipeline.

Can I switch LLM providers without changing MCP code?

Absolutely. Pydantic AI abstracts the model layer. your Sentinel Hub MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.