SkyFi MCP Server

A production-ready MCP server for SkyFi's satellite imagery Platform API. Search 150+ satellites from 12+ providers, compare pricing, and order archive or tasking imagery — all through natural language with any AI agent.

SkyFi aggregates imagery from Planet, Vantor, ICEYE, Umbra, Satellogic, and others into a single API with transparent pricing and no contracts. This MCP server makes that API accessible to Claude, GPT, Gemini, LangChain, and any MCP-compatible agent.

Architecture

graph LR
    A[AI Agent] -->|MCP Protocol| B[SkyFi MCP Server]
    B -->|REST API| C[SkyFi Platform API]
    B -->|Nominatim| K[OpenStreetMap]
    C --> D[Planet]
    C --> E[Vantor]
    C --> F[ICEYE]
    C --> G[Umbra]
    C --> H[Satellogic]
    C --> I[Sentinel-1/2]
    C --> J[12+ more...]

Quick Start (< 5 minutes)

1. Install

pip install skyfi-mcp
# or from source:
git clone https://github.com/jpwilson/skyfi-mcp.git
cd skyfi-mcp && pip install -e .

2. Configure

export SKYFI_API_KEY=your_api_key  # Get one at https://app.skyfi.com

3. Run

# Remote server (production)
skyfi-mcp
# → Listening at http://localhost:8000/mcp

# Local stdio (development)
SKYFI_MCP_TRANSPORT=stdio skyfi-mcp

4. Connect your agent

Claude Desktop — add to claude_desktop_config.json:

{
  "mcpServers": {
    "skyfi": {
      "url": "http://localhost:8000/mcp"
    }
  }
}

Claude Code:

claude mcp add skyfi http://localhost:8000/mcp

5. Try it (free)

"Find free Sentinel-2 imagery over the Amazon basin"

This uses open data ($0) — no credit card needed.

Tools (20)

Tool	Description
SkyFi API
skyfi_whoami	Check account status, budget, payment method
skyfi_search_archives	Search archive imagery by location, date, sensor, provider
skyfi_get_archive	Get full details for an archive image
skyfi_get_pricing	Get pricing by AOI, product type, provider
skyfi_create_feasibility	Create tasking feasibility check
skyfi_get_feasibility	Poll feasibility results and provider windows
skyfi_predict_passes	Satellite pass prediction for tasking timing
skyfi_create_archive_order	Stage an archive order (shows price, requires confirmation)
skyfi_create_tasking_order	Stage a tasking order (shows price, requires confirmation)
skyfi_confirm_order	Execute a staged order (this commits money)
skyfi_list_orders	List orders with status/type filters
skyfi_get_order	Get order details
skyfi_redeliver_order	Redeliver completed order to different cloud bucket
skyfi_get_deliverables	Get download URLs for completed orders
skyfi_create_notification	Set up AOI monitoring with webhook alerts
skyfi_list_notifications	List active monitoring notifications
skyfi_manage_notification	Get or delete a notification
OpenStreetMap
osm_geocode	Convert place names to coordinates + SkyFi-ready WKT polygons
osm_reverse_geocode	Convert coordinates to place names and addresses
osm_search_nearby	Find features (airports, ports, etc.) near a location

Ordering Safety

Orders use a two-step confirmation pattern:

1. Stage — skyfi_create_archive_order or skyfi_create_tasking_order calls the API and returns a price summary
2. Confirm — Only skyfi_confirm_order marks the order as human-approved

Order: VANTOR 30cm DAY, 12.4 sq km, $847. Deliver to S3. Confirm?

Free open-data orders ($0 Sentinel-1/2) still show confirmation but note "Free — no charge."

Tasking Workflow

1. skyfi_create_feasibility → get feasibility ID
2. skyfi_get_feasibility    → poll for provider windows
3. skyfi_predict_passes     → (optional) check satellite timing
4. skyfi_create_tasking_order → stage with selected provider/window
5. skyfi_confirm_order      → execute after human approval

Provider-specific: Planet supports provider_window_id selection from feasibility results.

AI Provider Integration

Provider	Method	Example
Claude Desktop	MCP config	Setup guide
Claude Code	claude mcp add	Setup guide
OpenAI	Function calling	examples/openai_example.py
Anthropic SDK	Tool use	examples/anthropic_example.py
Google Gemini	Function calling	examples/gemini_example.py
Google ADK	MCPToolset	examples/adk_example.py
LangChain	MCP adapter	examples/langchain_example.py
Vercel AI SDK	MCP client	Setup guide

Deployment

Railway (recommended for cloud)

railway login && railway init
railway variables set SKYFI_API_KEY=your_key
railway up
# → https://your-project.up.railway.app/mcp

Docker

docker build -t skyfi-mcp .
docker run -p 8000:8000 -e SKYFI_API_KEY=your_key skyfi-mcp

Or with docker-compose:

echo "SKYFI_API_KEY=your_key" > .env
docker-compose up

Configuration

Auth is resolved in priority order:

1. X-Skyfi-Api-Key request header (cloud deployment)
2. SKYFI_API_KEY environment variable
3. Config file at ~/.skyfi/config.json (or SKYFI_CONFIG_PATH)

Environment variables:

Variable	Default	Description
SKYFI_API_KEY	—	SkyFi Platform API key
SKYFI_MCP_HOST	0.0.0.0	Server bind host
SKYFI_MCP_PORT	8000	Server bind port
SKYFI_MCP_TRANSPORT	streamable-http	Transport: streamable-http, sse, stdio
SKYFI_API_BASE_URL	https://app.skyfi.com/platform-api	API base URL override

AOI Constraints

• Format: WKT POLYGON (convex)
• Max vertices: 500
• Max area: 500,000 sq km
• Coordinates: longitude latitude (WGS84)

Development

# Install with dev dependencies
pip install -e ".[dev]"

# Run tests
pytest

# Run with coverage
pytest --cov=skyfi_mcp --cov-report=term-missing

# Lint
ruff check src/ tests/

# Type check
mypy src/

# Security scan
bandit -r src/

Documentation

• Architecture
• Deployment Guide
• Provider Integration Guide
• API Mapping

Contributing

1. Fork the repository
2. Create a feature branch (git checkout -b feature/amazing-feature)
3. Write tests for new functionality
4. Run the full test suite (pytest)
5. Run linting and type checks (ruff check . && mypy src/)
6. Commit with descriptive messages
7. Open a pull request

License

MIT — see LICENSE.