How to Use the Maxar (Satellite Imagery) MCP in LangChain

Build automated geospatial chains in LangChain by linking Maxar satellite data directly into your reasoning pipelines.

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Connect Maxar (Satellite Imagery) MCP to LangChain

Create your Vinkius account to connect Maxar (Satellite Imagery) to LangChain 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 Maxar (Satellite Imagery) with LangChain

Ask AI about this MCP

ChatGPT

Claude

Perplexity

Chain Maxar discovery to imagery orders

Feed the output of `search_discovery` directly into `create_order` to automate your data acquisition. LangChain agents parse the catalog results to identify the exact coordinates needed for your specific AOI. Don't waste time manually checking availability. Define your agent's logic to trigger an order the moment a match meets your criteria, creating a continuous loop from search to delivery.

Integrate ARD tiles into multi-step chains

Pass metadata from `get_ard_tile` through your chain to downstream processing nodes. Your LangChain agent handles the coordinate mapping and tile retrieval without you writing boilerplate code. This keeps your pipeline focused on the final data output. You'll see exactly how the agent handles each step in your LangSmith traces.

Access streaming endpoints for live GIS

Use `get_mws_endpoints` to dynamically configure your GIS software via LangChain. The agent retrieves the necessary service URLs and injects them into your application state. This setup allows your system to switch between different Maxar streams based on the current context. It's a direct way to keep your mapping tools pointed at the right data.

Setup guide

Set up Maxar (Satellite Imagery) MCP in LangChain

Prerequisites

Python 3.10+ installed
langchain-mcp-adapters + langgraph packages
Active Vinkius subscription with a valid endpoint token

1

Install dependencies
Run pip install langchain-mcp-adapters langgraph langchain-openai. The MCP adapters package converts MCP tools into native LangChain BaseTool objects.
2

Connect via HTTP transport
Use MultiServerMCPClient with "transport": "http" pointing to your Vinkius endpoint. Replace [YOUR_TOKEN_HERE] with your token from cloud.vinkius.com.
3

Create a ReAct agent
Pass the discovered tools to create_react_agent() from LangGraph. The agent automatically routes Maxar (Satellite Imagery) tool calls through the MCP protocol.
4

Run with any LLM
Swap ChatOpenAI for ChatAnthropic, ChatGoogleGenerativeAI, or any LangChain-compatible model. The MCP tools work identically across all providers.

agent.py

from langchain_mcp_adapters.client import MultiServerMCPClient
from langgraph.prebuilt import create_react_agent
from langchain_openai import ChatOpenAI

async with MultiServerMCPClient({
    "maxar-satellite-imagery-mcp": {
        "transport": "http",
        "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp",
    }
}) as client:
    tools = client.get_tools()

    agent = create_react_agent(
        ChatOpenAI(model="gpt-4o"),
        tools,
    )
    result = await agent.ainvoke({
        "messages": "List recent Maxar (Satellite Imagery) transactions"
    })
    print(result["messages"][-1].content)

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Common questions about Maxar (Satellite Imagery) MCP in LangChain

You instantiate the MCP client and pass the tools to your agent. Use a search prompt to trigger `search_discovery` with your spatial parameters.

Yes. You can chain the discovery tool output to the order tool. The agent decides when the imagery quality meets your requirements.

Your connection uses an endpoint token managed by Vinkius. Data is handled only within your defined chain execution context.

The tools accept parameters for spatial and temporal constraints. You pass these directly from your agent's logic.

LangSmith logs every input and output for the MCP tools. You get full visibility into the agent's decision process.

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