INPE STAC API MCP for AI. Find Brazil's satellite metadata by date or region.
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INPE STAC API Satélites MCP gives you direct access to Brazil's National Institute for Space Research (INPE) satellite metadata. Query Earth observation catalogs—CBERS, Sentinel, Landsat—to find specific scenes, collections, and asset details using standard spatial and temporal filtering.
What your AI can do
List collection items
Lists specific individual assets within a collection using geographic or temporal filters.
Get collection
Retrieves detailed metadata for a single, named dataset collection.
List collections
Outputs a complete list of all data collections available in the catalog.
Determine the top-level structure of the INPE catalog using get_root_catalog.
List every available data set or collection, like CBERS4 or Sentinel-2, with list_collections.
Get detailed information about a specific data group using get_collection.
Find exact satellite scenes that match specific geographic boxes and dates with list_collection_items.
Run a search across multiple data collections to find any asset matching your criteria using search_items.
Verify the catalog structure against STAC standards with get_conformance.
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INPE STAC API Satélites: 6 Tools
These tools let you systematically discover, list, and retrieve metadata about specific satellite imagery collections and assets.
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Add this MCP to Claude, Cursor, or Windsurf and your AI stops guessing. It gets real tools to look things up, take action, and handle the stuff you keep doing by hand.
Start using INPE (STAC API - Satélites) on VinkiusList Collection Items
Lists specific individual assets within a collection using geographic or temporal filters.
Get Collection
Retrieves detailed metadata for a single, named dataset collection.
List Collections
Outputs a complete list of all data collections available in the catalog.
Get Conformance
Checks and reports on the STAC catalog standards compliance.
Get Root Catalog
Fetches the main, overarching structure of all available data catalogs.
Search Items
Searches for assets across multiple collections using specific coordinates and dates.
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Claude AI
Open Claude Settings
Go to claude.ai, click your profile icon, then navigate to Customize → Connectors.
Add Custom Connector
Click the "+" button and select Add custom connector. Paste your Vinkius endpoint URL:
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Choose How to Get Started
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Start with INPE (STAC API - Satélites), then connect any of our 5,100+ other servers whenever your AI needs more. One click, no limits.
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Works with Claude, ChatGPT, Cursor, and more
The Model Context Protocol standardizes how applications expose capabilities to LLMs. Instead of operating in isolation, your AI gains direct access to external platforms, live data, and real-world actions through secure, standardized connections.
This connection provides 6 powerful capabilities that interface natively with Claude, ChatGPT, Cursor, and other compatible AI platforms. No middleware. No custom integration required.
Getting satellite data used to be a nightmare of API keys and nested JSON files.
Today, pulling historical imagery requires navigating multiple government portals. You write scripts that call one endpoint just to get the list of available collections, then write another script with specific parameters (like bounding boxes) for each collection, cross-referencing dates and data types manually. It's hours of copy-pasting and debugging complex API documentation.
With this MCP, you simply tell your agent what you need—for example, 'I need all Sentinel scenes covering the coast last year.' The system handles the multi-step logic: it figures out which collections are relevant, searches them simultaneously, and returns a clean list of assets. You just get the answer.
Using INPE STAC API MCP for Metadata Discovery
The manual process of checking every collection manually is gone. You don't have to run list_collections, then check get_collection for each one, and finally write a separate search query for items.
You tell your agent the goal; it calls all the necessary tools in sequence behind the scenes. It’s instant data discovery.
What your AI can actually do with this
You need to know what’s up in the sky over a specific region of Brazil. This MCP connects your AI agent to INPE's massive data catalog. Instead of writing complex API calls or navigating dense government portals, you just ask natural questions: 'Show me all Sentinel-2 collections that covered São Paulo between January and March.' The system handles the technical heavy lifting, letting you discover which datasets exist, what they contain, and where to find the specific assets.
It's built on the STAC specification, so it’s predictable and reliable. If your agent needs access to deep geospatial data like this, Vinkius makes it simple; just connect it once and start querying everything from a single dashboard.
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Here's how it actually works
The bottom line is you tell your AI client what kind of satellite imagery you need, and it figures out exactly which collection holds that data.
First, subscribe to this MCP and supply your Brazil Data Cube (BDC) Access Key.
Next, prompt your agent to identify the available data collections using list_collections or get_root_catalog.
Finally, ask the agent to narrow down the results by providing a bounding box and date range using search_items.
Who is this actually for?
Environmental analysts who are tired of spending hours building custom API calls just to check deforestation rates. GIS developers needing a reliable metadata source for their applications. Data scientists who need quick, repeatable access to massive historical satellite archives.
Uses the MCP to monitor land use changes or track agricultural health by running complex searches with search_items and getting details via get_collection.
Integrates metadata discovery into a workflow, using list_collections to map out available data sources before calling list_collection_items for asset links.
Queries the catalog structure with get_root_catalog and uses get_conformance to ensure the data meets specific academic standards.
What Changes When You Connect
Skip manual querying. Instead of writing a multi-step script to find an asset, you ask your agent to search across collections using search_items and get the full list in one go.
Validate data integrity right away. Run get_conformance before committing to a workflow; it confirms that the catalog meets the expected STAC standards.
Avoid guesswork when sourcing data. Use list_collections first to see every available dataset, then use get_collection for specifics on any single one.
Focus only on what matters. Instead of browsing huge directories, you specify a bounding box and date range with list_collection_items to retrieve precise assets.
Build complex workflows quickly. Your agent can chain calls: first using get_root_catalog, then drilling into specific collections via get_collection.
See it in action
Tracking deforestation changes
An analyst needs to compare land cover from 2018 vs. 2023 in the Amazon basin. They ask their agent to search for items across collections using search_items, specifying both years and a large bounding box. The agent returns multiple matching scenes ready for comparison.
Developing a new visualization tool
A developer needs to know what data is available without writing code first. They use list_collections to map out all datasets (like CBERS4-WFI-16D-2) and then call get_collection for technical details on the top three candidates.
Investigating a recent weather event
A researcher wants data from a specific storm that happened last month. They use list_collection_items, filtering by the exact coordinates of the event and the precise date range to pull only relevant assets.
Verifying data source compliance
Before accepting a dataset for publication, the team lead uses get_conformance on the catalog. This confirms that all metadata structures adhere strictly to STAC protocol, saving time debugging broken schemas.
The honest tradeoffs
Trying to list items without knowing the collection.
Just asking 'Show me satellite data for Rio de Janeiro.' The system fails because it doesn't know which dataset (Sentinel, Landsat, etc.) to look in.
First, call list_collections to see all groups. Then, use get_collection on the correct group name, and finally, run search_items with your coordinates.
Using search_items for general browsing.
Searching across everything just because you are curious. This floods the agent with thousands of results, making it useless.
If you're browsing, start small. Use get_root_catalog to map out major sections, then use list_collections to narrow your focus.
Assuming all data is available in one place.
Calling a single tool expecting it to cover every time and place. This leads to incomplete results or API errors.
Always check the scope first. Use get_conformance to validate the structure, then use list_collections to see if multiple datasets are required.
When It Fits, When It Doesn't
Use this MCP if your core need is metadata discovery—you need to know that a scene exists, what its properties are, and where its link is. Don't use it if you need the actual high-resolution image file processed or analyzed; this tool only handles the catalog structure. If you just want general data on Brazil, look at generic geospatial APIs. But if you're dealing with specific INPE datasets (CBERS, Sentinel), this MCP gives you the structured querying capability you need. You use list_collections to find the source, then get_collection for details, and search_items when you have a target date/box.
Questions you might have
How do I find out what collections are available using list_collections? +
Simply prompt your agent to run list_collections. It will output a comprehensive list of all datasets, like CBERS4 and S2-16D-2, that you can then explore further.
Can I search across multiple data sources using search_items? +
Yes, this is exactly what search_items does. You provide a bounding box and a time range, and it searches all relevant collections simultaneously for matching assets.
What should I use to check the structure of the whole catalog? (get_root_catalog) +
Use get_root_catalog when you need the highest-level view. It provides the foundational map, showing all the major sections and how they relate before you drill down into specific data sets.
Is there a way to validate if the catalog adheres to standards? (get_conformance) +
Yes, running get_conformance validates the entire catalog structure. It confirms that the metadata follows the necessary STAC specifications, giving you confidence in the data integrity.
How do I narrow down my search for scenes using bounding boxes or dates with `list_collection_items`? +
You specify spatial and temporal filters in your request. This allows you to retrieve only the exact assets needed, rather than a full list of every item. It's essential for limiting results when monitoring specific geographical areas.
When I run `get_collection`, what kind of detailed metadata can I expect about that data set? +
You get the deep technical details, including sensor specifications, band names (like Blue or NIR), and coverage period. This information helps you understand exactly what kind of scientific data you're working with.
What should I do if my query fails when using `search_items` because the coordinates are outside the map area? +
The MCP will return a specific error message detailing why the search failed. You just need to adjust your bounding box parameters or check your coordinate system. The agent handles these failures gracefully.
What kind of access credentials are required before running any query, like `get_root_catalog`? +
You must first provide a valid Brazil Data Cube (BDC) Access Key when setting up the connection. This key authenticates your request and allows the agent to start querying the catalog.
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.
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