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

Validate all INPE satellite metadata at runtime with Pydantic AI and this MCP Server.

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Connect INPE (STAC API - Satélites) MCP to Pydantic AI

Create your Vinkius account to connect INPE (STAC API - Satélites) to Pydantic AI 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 Pydantic AI

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Perplexity

Strict metadata retrieval

Use `get_collection` to fetch data that Pydantic AI immediately validates against your defined models. If the schema shifts, your agent catches it instantly. Call `list_collections` to get the master list of datasets. The server returns the list, and your agent ensures every ID matches your expected format before proceeding.

Type-safe item searches

Execute `search_items` to find satellite imagery. Pydantic AI checks the result set against your schema, ensuring you don't feed malformed coordinates to your downstream code. Use `list_collection_items` to pull inventory. It provides the structured data you need, validated at the boundary to prevent silent errors in your logic.

Verify STAC compliance

Run `get_conformance` to check server capabilities. Pydantic AI validates the response so you know exactly which features are available to your agent. Call `get_root_catalog` to confirm your session is ready. It's a simple, typed check that guarantees your agent is starting from the correct root node.

Setup guide

Set up INPE (STAC API - Satélites) MCP in Pydantic AI

Prerequisites

Python 3.10+ installed
pydantic-ai-slim[fastmcp] package
Active Vinkius subscription with a valid endpoint token

1

Install Pydantic AI with FastMCP
Run pip install "pydantic-ai-slim[fastmcp]". The FastMCP toolset replaces the deprecated MCPServerHTTP class with full protocol support.
2

Configure the FastMCPToolset
Pass a JSON-style config dict to FastMCPToolset with your Vinkius URL. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com. Supports Streamable HTTP, SSE, and Stdio transports.
3

Create and run your agent
Pass the toolset to Agent(toolsets=[toolset]) and call agent.run(). Swap openai:gpt-4o for any supported model — Anthropic, Google, Mistral, or Groq.

agent.py

from pydantic_ai import Agent
from pydantic_ai.toolsets.fastmcp import FastMCPToolset

toolset = FastMCPToolset({
    "mcpServers": {
        "inpe-stac-api-satelites-mcp": {
            "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
        }
    }
})

agent = Agent(
    "openai:gpt-4o",
    toolsets=[toolset],
    system_prompt="You have access to INPE (STAC API - Satélites) tools.",
)

result = await agent.run("List recent INPE (STAC API - Satélites) transactions")
print(result.output)

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

Yes. Every tool response is checked against your Pydantic models at runtime. If the API returns invalid data, the agent stops immediately.

You define a Pydantic model that matches the STAC item schema. The MCP toolset handles the conversion and validation automatically.

Yes. It treats non-200 responses as standard Python exceptions, allowing you to catch and handle API downtime gracefully.

Yes, the MCP tools are model-agnostic. You can run them with any local or remote model supported by the framework.

The server uses a single endpoint token for authentication. Your sensitive API keys never touch the agent's memory or the model's context.

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