mcp-stargazing 0.7.1

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).
• Composite Planning: Build a ranked observing plan that combines place quality, weather, moonlight, and top targets.
• Tool Discovery: Inspect registered MCP tools programmatically through get_tool_catalog.
• 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.
  • Stable Result Handles: analysis_area.resource_id identifies the cached non-pagination query so agents can fetch multiple pages safely.
  • Standardized Responses: Successful calls return { "data": ..., "_meta": ... }; business validation failures return { "error": ..., "_meta": ... }.
• 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 (MCP on :3001 + SPF web UI on :5001)
   docker run -p 3001:3001 -p 5001:5001 mcp-stargazing
   
   # With Environment Variables
   docker run -p 3001:3001 -p 5001:5001 \
     -e QWEATHER_API_KEY=your_key \
     -e STARGAZING_DB_CONFIG=your_db_config \
     mcp-stargazing
   

3. Access:

   • MCP server → http://localhost:3001/shttp (for AI agent / MCP client)
   • SPF web UI → http://localhost:5001/ (stargazing place finder frontend)

Docker Architecture

The container runs two services managed by supervisord, both sharing the same Python virtual environment via uv run:

Container
├── supervisord
│   ├── program:mcp      → uv run mcp-stargazing --mode shttp --port 3001
│   └── program:spf-web  → uv run uvicorn server.main:app --port 5001
│
Dependency resolution (single venv, no duplication):
  stargazing-core>=0.1.0  ←  resolved once from PyPI
  stargazing-place-finder>=0.8.0
  fastapi, uvicorn, ...   ←  SPF's transitive deps

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

dev mode is no longer supported because current FastMCP versions no longer provide run_dev(). Use local, shttp, or sse.

3. Response Format

Successful business responses 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"
  }
}

Business validation failures use the same envelope style:

{
  "error": {
    "code": "INVALID_TIME_FORMAT",
    "message": "Invalid time format: invalid-time-format",
    "details": {
      "time_string": "invalid-time-format"
    }
  },
  "_meta": {
    "version": "1.0.0",
    "status": "error"
  }
}

At the MCP protocol layer, tools/list and get_tool_catalog are kept aligned, and JSON-RPC request ids are preserved in both SHTTP and SSE transport tests.

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.
• list_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.
• get_best_stargazing_plan: Build a ranked regional observing plan with candidate places, weather summaries, best observation windows, and top targets.
  • Inputs: south, west, north, east, time, time_zone, candidate_limit, target_limit, weather_provider, max_locations, min_height_diff, road_radius_km, network_type, db_config_path.
  • Returns: query, summary, and candidates, where query.analysis_resource_id links the plan back to the underlying analysis_area search when available.
  • Degradation: Weather or forecast sub-queries may degrade into summary.warnings and per-candidate notes, while the overall planning response remains successful.
• get_telescope_targets: Match deep-sky objects against telescope optics — find what's best visible with your equipment.
  • Inputs: telescope (preset name or custom config), ra/dec or target_name, time, time_zone.
  • Returns: Ranked list of observable targets with visibility scores, altitude/azimuth, and telescope-specific framing.
• get_shooting_plan: Generate an optimized imaging schedule for a target, maximizing time above altitude threshold.
  • Inputs: target_name or ra/dec, telescope, time, time_zone, duration_hours, min_altitude_deg.
  • Returns: Time-ordered sequence of exposures with meridian flip warnings and moon separation data.
• light_pollution_map: Query light pollution data for a bounding box area.
  • Inputs: south, west, north, east, zoom (default 10).
  • Returns: A grid of data points with Bortle class, brightness, and SQM values.
• analysis_area: Find best stargazing spots in a region.
  • Inputs: south, west, north, east, max_locations, min_height_diff, road_radius_km, network_type, db_config_path, page, page_size.
  • Returns: List of spots with pagination metadata (total, page, page_size, total_pages) and a resource_id that identifies the cached non-pagination query parameters.
  • Validation: page >= 1 and page_size >= 1; invalid pagination arguments return CONFIGURATION_ERROR.

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)
• Business Error Responses: Structured MCPError-derived payloads with actionable messages for calling agents
• Protocol Tests: tools/list, get_tool_catalog, and SSE request-id behavior are covered by protocol-level tests
• 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

.
├── src/
│   ├── functions/            # Tool implementations grouped by domain
│   │   ├── celestial/        # Celestial calculations (pos, rise/set)
│   │   ├── metadata/         # Tool discovery surface (`get_tool_catalog`)
│   │   ├── planning/         # Composite planning tools (`get_best_stargazing_plan`)
│   │   ├── telescope/        # Telescope target matching + shooting plan
│   │   ├── weather/          # Weather API integration
│   │   ├── places/           # Location and area analysis
│   │   └── time/             # Time utilities
│   ├── schemas/              # Pydantic v2 data models
│   ├── 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 # Legacy QWeather helpers
├── tests/                    # Unified test suite (25+ test files)
├── examples/                 # Usage examples (14 scripts)
├── docs/                     # Design docs and roadmap
├── Dockerfile                # Multi-stage Docker build
├── supervisord.conf          # Dual-service process manager config
└── pyproject.toml            # Project configuration and dependencies

Testing

Run the unified test suite:

uv run pytest -v 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.
• test_mcp_client.py: Verifies tools/list, tools/call, and SSE request-id protocol behavior.
• test_structured_errors.py: Verifies business validation failures stay in the structured response envelope.

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.