AI-powered cargo management and optimization endpoints
Universal MCP endpoint
MCP Endpoint
Copy the url to connect any MCP-compatible AI tool
https://api.cosmocargo.space/ai/v1/mcp
AI Tool Configuration
Choose your AI tool and copy the configuration to get started.
- Open Claude Desktop and click Settings in the lower corner
- Navigate to Developer tab → Edit Config
- Add this configuration to
claude_desktop_config.json
:claude_desktop_config.json - Save the file and restart Claude Desktop. Look for the 🔨 icon in the bottom-right corner.
macOS: ~/Library/Application Support/Claude/claude_desktop_config.json
Windows: %APPDATA%\Claude\claude_desktop_config.json
Optimize cargo routing
Uses advanced AI algorithms to find the optimal route for cargo shipments across the solar system, considering factors like transit time, cost, and special cargo requirements.
Request Body
origin
string · requiredOrigin space station or planet
Example: Earth Station Alphadestination
string · requiredDestination space station or planet
Example: Mars Colony Betacargo
object[] · required
constraints
object
Responses
Optimization completed successfully
recommendedRoute
objectestimatedCost
number · floatCost in Galactic Credits
estimatedTime
integerTransit time in Earth days
confidenceScore
number · float · min: 0 · max: 1alternativeRoutes
object[]
Predict shipment issues
Uses machine learning to predict potential issues with shipments including delays, damage risks, and route disruptions based on historical data and current space weather conditions.
Request Body
shipmentId
string · uuid · required
predictionTypes
string[]Enum values:DELAYDAMAGEROUTE_DISRUPTIONCOST_OVERRUN
Responses
Predictions generated successfully
predictions
object[]overallRiskLevel
string · enumEnum values:LOWMEDIUMHIGHCRITICAL
Analyze fleet performance
Provides AI-powered analytics on fleet performance including fuel efficiency, delivery times, and maintenance predictions.
query Parameters
timeRange
string · enumEnum values:7d30d90d1yDefault: 30dfleetId
string
Responses
Fleet analysis completed
overallScore
number · float · min: 0 · max: 100insights
object[]