Bring Satellite Imagery
to Pydantic AI
Learn how to connect INPE (STAC API - Satélites) to Pydantic AI and start using 6 AI agent tools in minutes. Fully managed, enterprise secure, and ready to use without writing a single line of code.
GDPR Free for SubscribersCompatible with every major AI agent and IDE
Gemini
What is the INPE (STAC API - Satélites) MCP Server?
Connect to the INPE Brazil Data Cube and explore high-resolution satellite imagery catalogs through natural language. This server implements the STAC (SpatioTemporal Asset Catalog) specification to provide seamless access to Earth observation data from missions like CBERS, Sentinel, and Landsat.
What you can do
- Catalog Discovery — Explore the root catalog and check conformance classes of the INPE STAC server using
get_root_catalogandget_conformance. - Collection Browsing — List all available data collections (e.g., CBERS4, Sentinel-2) and fetch detailed metadata for specific ones with
list_collectionsandget_collection. - Item Listing — Retrieve specific scenes and assets within a collection using spatial (bounding box) and temporal filters via
list_collection_items. - Advanced Search — Perform cross-collection searches to find the exact satellite imagery needed for environmental monitoring or research using
search_items.
How it works
- Subscribe to this server
- Enter your Brazil Data Cube (BDC) Access Key
- Start querying satellite metadata from Claude, Cursor, or any MCP-compatible client
Who is this for?
- Data Scientists & Researchers — Quickly find satellite scenes for specific dates and regions without manual API calls or complex scripts.
- Environmental Analysts — Monitor land use, deforestation, or agricultural changes using INPE's curated data cubes directly from your AI assistant.
- GIS Developers — Integrate satellite metadata discovery and asset retrieval directly into your development workflow.
Built-in capabilities (6)
Get detailed metadata for a specific collection
Get STAC conformance classes
Get the root STAC catalog
List items within a specific collection
g., CBERS4-WFI-16D-2, S2-16D-2). List all available data collections
Search for items across collections
Why Pydantic AI?
Pydantic AI validates every INPE (STAC API - Satélites) tool response against typed schemas, catching data inconsistencies at build time. Connect 6 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.
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Full type safety: every MCP tool response is validated against Pydantic models, catching data inconsistencies before they reach your application
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Model-agnostic architecture. switch between OpenAI, Anthropic, or Gemini without changing your INPE (STAC API - Satélites) integration code
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Structured output guarantee: Pydantic AI ensures tool results conform to defined schemas, eliminating runtime type errors
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Dependency injection system cleanly separates your INPE (STAC API - Satélites) connection logic from agent behavior for testable, maintainable code
INPE (STAC API - Satélites) in Pydantic AI
INPE (STAC API - Satélites) and 4,000+ other MCP servers. One platform. One governance layer.
Teams that connect INPE (STAC API - Satélites) to Pydantic AI through Vinkius don't need to source, host, or maintain individual MCP servers. Every tool call runs inside a hardened runtime with credential isolation, DLP, and a signed audit chain.
Raw MCP | Vinkius | |
|---|---|---|
| Server catalog | Find and host yourself | 4,000+ managed |
| Infrastructure | Self-hosted | Sandboxed V8 isolates |
| Credential handling | Plaintext in config | Vault + runtime injection |
| Data loss prevention | None | Configurable DLP policies |
| Kill switch | None | Global instant shutdown |
| Financial circuit breakers | None | Per-server limits + alerts |
| Audit trail | None | Ed25519 signed logs |
| SIEM log streaming | None | Splunk, Datadog, Webhook |
| Honeytokens | None | Canary alerts on leak |
| Custom domains | Not applicable | DNS challenge verified |
| GDPR compliance | Manual effort | Automated purge + export |
Why teams choose Vinkius for INPE (STAC API - Satélites) in Pydantic AI
The INPE (STAC API - Satélites) 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. All 6 tools execute in hardened sandboxes optimized for native MCP execution.
Your AI agents in Pydantic AI only access the data you authorize, with DLP that blocks sensitive information from ever reaching the model, kill switch for instant shutdown, and up to 60% token savings. Enterprise-grade infrastructure, zero maintenance.
* 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
How Vinkius secures
INPE (STAC API - Satélites) for Pydantic AI
Every tool call from Pydantic AI to the INPE (STAC API - Satélites) MCP Server is protected by DLP redaction, cryptographic audit chains, V8 sandbox isolation, kill switch, and financial circuit breakers.
Frequently asked questions
How can I see what types of satellite data are available?
Use the list_collections tool. It will return a list of all available datasets in the INPE Brazil Data Cube, such as CBERS-4, Sentinel-2, and various synthesized data cubes.
Can I search for specific images using geographic coordinates?
Yes! Use the search_items tool and provide a bbox (bounding box) string in the format '[minx, miny, maxx, maxy]'. This allows you to find all satellite scenes covering your area of interest.
How do I get the technical metadata for a specific collection?
Use the get_collection tool with the specific collection_id (e.g., 'CBERS4-WFI-16D-2'). It will provide detailed information about the spatial extent, temporal interval, and available assets for that collection.
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 INPE (STAC API - Satélites) MCP integration works identically with OpenAI, Anthropic, Google, or any supported provider.
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Update: pip install --upgrade pydantic-ai
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