mcp-stargazing 0.2.0

An MCP server that provides real-time astronomical data, smart stargazing planning, and light pollution analysis for AI agents.

mcp-stargazing

Calculate the altitude, rise, and set times of celestial objects (Sun, Moon, planets, stars, and deep-space objects) for any location on Earth, with optional light pollution analysis.

Features

• Altitude/Azimuth Calculation: Get elevation and compass direction for any celestial object.
• Rise/Set Times: Determine when objects appear/disappear above the horizon.
• Light Pollution Analysis: Load and analyze light pollution maps (GeoTIFF format).
• Code Execution Ready:
  • Serializable Returns: All tools return JSON-serializable data (ISO strings for dates), making them directly usable by LLMs.
  • Pagination: analysis_area supports paging (page, page_size) to handle large datasets efficiently.
  • Standardized Responses: Uniform response format { "data": ..., "_meta": ... } for better observability and error handling.
• Performance:
  • Async Execution: Non-blocking celestial calculations.
  • Caching: Intelligent caching for Simbad queries and regional analysis.
  • Proxy Support: Native support for HTTP/HTTPS proxies (useful for downloading astronomical data).
• Time Zone Aware: Works with local or UTC times.
• Data Driven: Integrated database of 10,000+ deep-sky objects (Messier & NGC) for smart recommendations.

Installation

This project uses uv for dependency management.

Local Installation

1. Install uv:

   pip install uv
   

2. Sync dependencies:

   uv sync
   

   This will create a virtual environment in .venv and install all dependencies defined in pyproject.toml.

3. Activate the environment:

   source .venv/bin/activate
   

4. Initialize Data (Required for Nightly Planner): This downloads the latest Messier and NGC catalog data to src/data/objects.json.

   python scripts/download_data.py
   

   Note: If you are behind a firewall, ensure HTTP_PROXY env var is set before running this script.

Docker Installation

You can also run the server using Docker, which handles all dependencies and data initialization automatically.

1. Build the image:

   docker build -t mcp-stargazing .
   

   Note: If you are behind a proxy, pass the proxy URL during build:

   docker build --build-arg HTTP_PROXY=http://127.0.0.1:7890 -t mcp-stargazing .
   

2. Run the container:

   # Basic run (SHTTP mode on port 3001)
   docker run -p 3001:3001 mcp-stargazing
   
   # With Environment Variables
   docker run -p 3001:3001 \
     -e QWEATHER_API_KEY=your_key \
     -e STARGAZING_DB_CONFIG=your_db_config \
     mcp-stargazing
   

MCP Server Usage

Start the MCP server to expose tools to AI agents or other clients.

1. Environment Setup

Create a .env file or export variables:

# Weather tools
# 推荐：使用你账号专属的 API Host（公共域名将从 2026 年起逐步停止服务）
export QWEATHER_API_HOST="abc1234xyz.def.qweatherapi.com"

# 鉴权（二选一）
# 1) API KEY（兼容旧用法）
export QWEATHER_API_KEY="your_api_key"
# 2) JWT（推荐，更安全）
# export QWEATHER_JWT_TOKEN="your_jwt_token"

# 如需临时兼容旧公共域名（不推荐），显式开启：
# export QWEATHER_ALLOW_PUBLIC_HOST=1

# Optional: Proxy for downloading astronomical data (Simbad/IERS)
# Highly recommended if you are in a restricted network environment
export HTTP_PROXY="http://127.0.0.1:7890"
export HTTPS_PROXY="http://127.0.0.1:7890"

2. Start Server

Streamable HTTP (SHTTP) mode (Recommended for most agents):

# Basic start
python -m src.main --mode shttp --port 3001 --path /shttp

# With proxy explicitly passed (overrides env vars)
python -m src.main --mode shttp --port 3001 --path /shttp --proxy http://127.0.0.1:7890

SSE mode:

python -m src.main --mode sse --port 3001 --path /sse

3. Response Format

All tools return data in a standardized JSON format:

{
  "data": {
    // Tool-specific return data
    "altitude": 45.5,
    "azimuth": 180.0
  },
  "_meta": {
    "version": "1.0.0",
    "status": "success"
  }
}

4. Available Tools

• get_celestial_pos: Calculate altitude/azimuth.
• get_celestial_rise_set: Calculate rise/set times (Returns ISO strings).
• get_moon_info: Detailed moon phase, illumination, and age.
• get_visible_planets: List of all planets currently above the horizon with positions.
• get_constellation: Find the position (Alt/Az) of a constellation center.
• get_nightly_forecast: Smart planner returning curated list of best objects to view tonight (Planets + Deep Sky).
• get_weather_by_name / get_weather_by_position: Fetch current weather with automatic retry on network failures.
• get_local_datetime_info: Get current local time information.
• get_tool_catalog: Discover available MCP tool metadata and parameters.
• analysis_area: Find best stargazing spots in a region.
  • Inputs: top_left, bottom_right, time, page, page_size.
  • Returns: List of spots with viewing conditions, plus pagination metadata (total, resource_id).

5. Error Handling

All tools return JSON-serializable data and use structured error handling:

• Standard Error Codes: INVALID_COORDINATES, INVALID_TIMEZONE, INVALID_TIME_FORMAT, MISSING_API_KEY, API_AUTH_FAILURE, API_TIMEOUT, API_RATE_LIMIT, EXTERNAL_API_ERROR, NETWORK_ERROR, CONFIGURATION_ERROR
• Weather Tools: Include automatic retry logic for network failures (up to 3 attempts with exponential backoff)
• Error Responses: Structured MCPError objects with actionable error messages for calling agents
• Validation: Input parameters are validated before processing with clear error messages

Examples

• Nightly Planner: python examples/nightly_forecast_demo.py

  • Shows a curated list of planets and deep-sky objects visible tonight, accounting for moonlight.

• Visible Planets: python examples/visible_planets_demo.py

  • Lists which planets are currently up.

• Moon Info: python examples/moon_phase_demo.py

  • Prints a 30-day moon phase calendar.

• Orchestration: python examples/code_execution_orchestration.py

  • Demonstrates a full workflow: Get time -> Get Celestial Pos -> Check Weather -> Find Spots.
  • Shows how to handle the standardized response format programmatically.

• Pagination: python examples/pagination_demo.py

  • Demonstrates fetching large result sets page by page using the resource_id.

Project Structure

The project is modularized for better maintainability and code execution support:

.
├── src/
│   ├── functions/            # Tool implementations grouped by domain
│   │   ├── celestial/        # Celestial calculations (pos, rise/set)
│   │   ├── weather/          # Weather API integration
│   │   ├── places/           # Location and area analysis
│   │   └── time/             # Time utilities
│   ├── cache.py              # Caching logic for analysis results
│   ├── response.py           # Standardized response formatting
│   ├── server_instance.py    # FastMCP server instance (avoids circular imports)
│   ├── main.py               # Entry point and tool registration
│   ├── celestial.py          # Core astronomy logic (Astropy wrappers)
│   ├── placefinder.py        # Grid analysis logic
│   └── qweather_interaction.py # Weather API client
├── tests/                    # Unified test suite
│   ├── test_celestial.py
│   ├── test_weather.py
│   ├── test_serialization.py # Validates JSON return formats
│   └── test_integration.py   # End-to-end flow tests
├── examples/                 # Usage examples
├── docs/                     # Documentation and improvement plans
└── pyproject.toml            # Project configuration and dependencies

Testing

Run the unified test suite:

pytest tests/

Key tests include:

• test_serialization.py: Ensures all tools return valid JSON with the correct schema.
• test_integration.py: Mocks external APIs to verify the entire toolchain.

Contributing

1. Follow the Code Execution with MCP best practices.
2. Ensure all new tools return standard JSON responses using src.response.format_response.
3. Add tests in tests/ for any new functionality.
4. Follow the repository agent conventions in AGENTS.md for all MCP tool and agent-facing changes.
5. Refer to docs/ROADMAP.md for the planned agent and harness feature roadmap.