How to Use the INPE (STAC API - Satélites) MCP in LlamaIndex

Q: How do I connect the INPE (STAC API - Satélites) MCP Server to LlamaIndex?

Install the llama-index-tools-mcp package and initialize the basic MCP client with your Vinkius endpoint. Convert the client into tool specs and pass them to your LlamaIndex FunctionAgent to start querying satellite data.

Q: Can I index INPE (STAC API - Satélites) metadata into a vector store with LlamaIndex?

Yes, you can fetch metadata using getcollection or listcollections and load the text directly into a LlamaIndex document parser. This lets you run semantic search over satellite collection descriptions to find the right dataset.

Q: Does the INPE (STAC API - Satélites) MCP Server support LLM routing in LlamaIndex?

Yes. LlamaIndex uses the tool schemas for searchitems and listcollectionitems to let the router agent decide when to query the satellite API based on the user's geographic questions.

Index INPE satellite metadata directly into your LlamaIndex vector stores for hallucination-free spatial RAG with this managed MCP Server.

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Connect INPE (STAC API - Satélites) MCP to LlamaIndex

Create your Vinkius account to connect INPE (STAC API - Satélites) to LlamaIndex and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup INPE (STAC API - Satélites) with LlamaIndex

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Index catalogs into LlamaIndex vector stores

The `list_collections` tool pulls actual satellite catalog metadata directly into your LlamaIndex vector database. When a user asks about Amazonia-1 coverage, your agent performs semantic search over the indexed catalog metadata and retrieves details using `get_collection`. Stop letting your agent guess which satellite collections exist. This method grounds your RAG applications in real geographic schemas rather than outdated training data.

Ground your agent in live spatial metadata

The `list_collection_items` tool lets your LlamaIndex query engine pull real-time satellite item lists on the fly. The agent uses `get_root_catalog` to map out the STAC hierarchy before performing deep queries. By wrapping this tool, your RAG pipeline can inject current image timestamps directly into the LLM context window. This ensures your application always references valid Brazilian space agency data structures.

Execute precise spatial RAG queries

The `search_items` tool finds CBERS-4 imagery matching specific bounding boxes and dates, then feeds those results into your LlamaIndex indexer. Your pipeline can run `get_conformance` to check which search extensions are supported before building complex spatial queries. This combines static document retrieval with live, verified Earth observation data. This prevents runtime errors when executing advanced filtering logic on geographic datasets.

Setup guide

Set up INPE (STAC API - Satélites) MCP in LlamaIndex

Prerequisites

Python 3.10+ installed
llama-index-tools-mcp package
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install llama-index-tools-mcp llama-index-llms-openai. The MCP tools package provides BasicMCPClient and McpToolSpec.
2

Connect with BasicMCPClient
Point BasicMCPClient to your Vinkius endpoint URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports SSE and Streamable HTTP transports.
3

Convert to LlamaIndex tools
Call mcp_tool_spec.to_tool_list_async() to convert all INPE (STAC API - Satélites) MCP tools into native FunctionTool objects that any LlamaIndex agent can use.
4

Run with any LLM
Create a FunctionAgent with the tools and your preferred LLM. Swap OpenAI for Anthropic, Gemini, or any LlamaIndex-supported provider.

agent.py

from llama_index.tools.mcp import BasicMCPClient, McpToolSpec
from llama_index.core.agent.workflow import FunctionAgent
from llama_index.llms.openai import OpenAI

# Connect to the MCP
mcp_client = BasicMCPClient(
    "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
)
mcp_tool_spec = McpToolSpec(client=mcp_client)

# Convert MCP tools to LlamaIndex tools
tools = await mcp_tool_spec.to_tool_list_async()

# Create and run the agent
agent = FunctionAgent(
    tools=tools,
    llm=OpenAI(model="gpt-4o"),
    system_prompt="You have access to INPE (STAC API - Satélites) tools.",
)
response = await agent.run("List recent INPE (STAC API - Satélites) data")

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Independent Platform Disclaimer: Vinkius is an independent platform and is not affiliated with, endorsed by, sponsored by, verified by, or otherwise authorized by INPE (STAC API). All third-party trademarks, logos, and brand names are the property of their respective owners. Their use on this website is strictly for informational purposes to identify service compatibility and interoperability.

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Common questions about INPE (STAC API - Satélites) MCP in LlamaIndex

Install the `llama-index-tools-mcp` package and initialize the basic MCP client with your Vinkius endpoint. Convert the client into tool specs and pass them to your LlamaIndex `FunctionAgent` to start querying satellite data.

Yes, you can fetch metadata using `get_collection` or `list_collections` and load the text directly into a LlamaIndex document parser. This lets you run semantic search over satellite collection descriptions to find the right dataset.

Yes. LlamaIndex uses the tool schemas for `search_items` and `list_collection_items` to let the router agent decide when to query the satellite API based on the user's geographic questions.

You can configure the search parameters within your LlamaIndex agent or pass limit arguments directly to `search_items`. This keeps the returned list of satellite assets small enough to fit comfortably inside your LLM context window.

All requests to the INPE catalog run through an ephemeral V8 sandbox that is destroyed immediately after execution. Your API tokens and queried geographic coordinates are never written to persistent storage, ensuring absolute isolation from other users.

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