Smithsonian Open Access MCP Server

# Smithsonian Open Access MCP Server [](https://badge.fury.io/js/%40molanojustin%2Fsmithsonian-mcp) [](https://www.npmjs.com/package/@molanojustin%2Fsmithsonian-mcp) [](https://hub.docker.com/r/justinmol/smithsonian-mcp) A **Model Context Protocol (MCP)** server that provides AI assistants with access to the **Smithsonian Institution's Open Access collections**. This server allows AI tools like Claude Desktop to search, explore, and analyze over 3 million collection objects from America's national museums. ## Quick Start ### Option 1: npm/npx Installation (Easiest) The npm package includes automatic Python dependency management and works across platforms: ```bash # Install globally npm install -g @molanojustin/smithsonian-mcp # Or run directly with npx (no installation needed) npx -y @molanojustin/smithsonian-mcp # Set your API key export SMITHSONIAN_API_KEY=your_key_here # Start the server smithsonian-mcp ``` ### Option 2: Automated Setup (Recommended for Python users) The enhanced setup script now includes: - ✅ **API key validation** - Tests your key before saving - ✅ **Service installation** - Auto-install as system service - ✅ **Claude Desktop config** - Automatic configuration - ✅ **Health checks** - Verify everything works **macOS/Linux:** ```bash chmod +x config/setup.sh config/setup.sh ``` **Windows:** ```powershell config\setup.ps1 ``` ### Option 3: Manual Setup 1. **Get API Key**: [api.data.gov/signup](https://api.data.gov/signup/) (free) 2. **Install**: `uv pip install -r config/requirements.txt` 3. **Configure**: Copy `.env.example` to `.env` and set your API key 4. **Test**: `python examples/test-api-connection.py` ### Verify Setup Run the verification script to check your installation: ```bash python scripts/verify-setup.py ``` ## Features ### Core Functionality - **Search Collections**: 3+ million objects across 24 Smithsonian museums - **Object Details**: Complete metadata, descriptions, and provenance - **On-View Status** - Find objects currently on physical exhibit - **Image Access**: High-resolution images (CC0 licensed when available) - **Museum Information**: Browse all Smithsonian institutions - **Collection Statistics**: Comprehensive metrics with per-museum breakdowns (sampling-based estimates) ### AI Integration - **16 MCP Tools**: Smart discovery, comprehensive search, museum-specific queries, exhibition status, contextual data access, and proactive collection type discovery - **Proactive Discovery**: New tools help AI assistants understand API scope and available object types before searching, preventing confusion about archival vs. museum materials - **Smart Context**: Contextual data sources for AI assistants including enhanced statistics - **Rich Metadata**: Complete object information and exhibition details - **Exhibition Planning** - Tools to find and explore currently exhibited objects - **Collection Analytics**: Per-museum statistics with sampling-based accuracy - **Multi-Model Compatible**: Works well with both advanced and simpler AI models through simplified tool interfaces ### URL Validation & Anti-Guessing - **Easiest Solution**: Use `search_and_get_first_url()` for one-step search + validated URL retrieval - **Mandatory Tool Usage**: LLM must use `get_object_url()` tool for any URL retrieval - manual construction fails due to case sensitivity - **Flexible Identifiers**: Supports Accession Numbers (F1900.47), Record IDs (fsg_F1900.47), and Internal IDs (ld1-...) - **URL Validation**: Automatically selects authoritative record_link over API identifiers, handles case sensitivity ## Integration ### Claude Desktop #### Option 1: Using npm/npx (Recommended) 1. **Configure** (`claude_desktop_config.json`): ```json { "mcpServers": { "smithsonian_open_access": { "command": "npx", "args": ["-y", "@molanojustin/smithsonian-mcp"], "env": { "SMITHSONIAN_API_KEY": "your_key_here" } } } } ``` #### Option 2: Using Python installation 1. **Configure** (`claude_desktop_config.json`): ```json { "mcpServers": { "smithsonian_open_access": { "command": "python", "args": ["-m", "smithsonian_mcp.server"], "env": { "SMITHSONIAN_API_KEY": "your_key_here" } } } } ``` 2. **Test**: Ask Claude "What Smithsonian museums are available?" ### mcpo Integration (MCP Orchestrator) **mcpo** is an MCP orchestrator that converts multiple MCP servers into OpenAPI/HTTP endpoints, ideal for combining multiple services into a single systemd service. #### Installation ```bash # Install mcpo uvx mcpo # Or using uvx uvx mcpo --help ``` #### Configuration Create a `examples/mcpo-config.json` file: ```json { "mcpServers": { "smithsonian_open_access": { "command": "python", "args": ["-m", "smithsonian_mcp.main"], "env": { "SMITHSONIAN_API_KEY": "your_api_key_here" } }, "memory": { "command": "npx", "args": ["-y", "@modelcontextprotocol/server-memory"] }, "time": { "command": "uvx", "args": ["mcp-server-time", "--local-timezone=America/New_York"] } } } ``` #### Running with mcpo ```bash # Start mcpo with hot-reload mcpo --config examples/mcpo-config.json --port 8000 --hot-reload # With API key authentication mcpo --config examples/mcpo-config.json --port 8000 --api-key "your_secret_key" # Access endpoints: # - Smithsonian: http://localhost:8000/smithsonian_open_access # - Memory: http://localhost:8000/memory # - Time: http://localhost:8000/time # - API docs: http://localhost:8000/docs ``` #### Systemd Service Create `/etc/systemd/system/mcpo.service`: ```ini [Unit] Description=MCP Orchestrator Service After=network.target [Service] Type=simple User=your-user WorkingDirectory=/path/to/your/config Environment=PATH=/path/to/venv/bin ExecStart=/path/to/venv/bin/mcpo --config examples/mcpo-config.json --port 8000 Restart=always RestartSec=10 [Install] WantedBy=multi-user.target ``` ```bash # Enable and start service sudo systemctl enable mcpo sudo systemctl start mcpo sudo systemctl status mcpo ``` #### Troubleshooting mcpo See [TROUBLESHOOTING.md](TROUBLESHOOTING.md) for detailed mcpo troubleshooting, including: - ModuleNotFoundError solutions - Connection closed errors - Port conflicts - Path configuration issues ### VS Code 1. **Open Workspace**: `code .vscode/smithsonian-mcp-workspace.code-workspace` 2. **Run Tasks**: Debug, test, and develop the MCP server 3. **Claude Code**: AI-assisted development with Smithsonian data ## Available Data - **19 Museums**: NMNH, NPG, SAAM, NASM, NMAH, and more - **3+ Million Objects**: Digitized collection items - **CC0 Content**: Public domain materials for commercial use - **Rich Metadata**: Creators, dates, materials, dimensions - **High-Resolution Images**: Professional photography ### Data Accuracy & Sampling Collection statistics for objects with images use **sampling methodology** to provide accurate estimates: - **Sample Size**: Up to 1000 objects per query for statistical significance - **Methodology**: Counts actual returned objects instead of relying on potentially buggy API totals - **Coverage**: Includes per-museum breakdowns with individual sampling for each institution - **Transparency**: All sampled counts are clearly marked as "(est.)" in outputs This approach ensures reliable metrics while respecting API rate limits and avoiding the Smithsonian API's rowCount filtering bug. ### Current API Limitations **Image URLs Not Available**: The Smithsonian Open Access API currently does not provide image URLs or media data in detailed content responses. While the search API can filter objects by media type (e.g., `online_media_type:Images`), the actual image URLs are not included in the detailed object data returned by the content API. This appears to be a change in the API since the available documentation was published. - Objects will show as having 0 images even when filtered for image content - Image statistics are estimates based on search filtering, not actual media availability - The system gracefully handles this limitation and continues to provide all other metadata **API Scope: Diverse Museum Collections**: The Smithsonian Open Access API provides access to diverse collections across 24 Smithsonian museums, with each museum having distinct object types reflecting their unique focus areas. The discovery tools now correctly identify museum-specific collections with comprehensive object type intelligence gathered through systematic sampling. - **SAAM** (American Art): Paintings, decorative arts, sculptures, drawings - **NASM** (Air & Space): Aircraft, avionics, spacecraft, aviation equipment - **NMAH** (American History): Historical artifacts, inventions, cultural objects - **CHNDM** (Design Museum): Design objects, textiles, furniture, graphics - Use discovery tools (`get_museum_collection_types`, `check_museum_has_object_type`) to explore available collections - Each museum's collection reflects its institutional mission and expertise ## MCP Tools ### Search & Discovery - `simple_explore` - Smart diverse sampling across museums and object types (recommended for general discovery) - `continue_explore` - Get more results about the same topic while avoiding duplicates - `search_collections` - Advanced search with filters (prioritizes museum-specific results when unit_code specified) - `search_and_get_first_url` - **Easiest option**: Search and get validated URL in one step (prevents manual URL construction) - `get_object_details` - Detailed object information - `get_object_url` - Get validated object URLs with flexible identifier support (MANDATORY: never construct URLs manually) - `search_by_unit` - Museum-specific searches - `get_objects_on_view` - Find objects currently on physical exhibit - `check_object_on_view` - Check if a specific object is on display - `get_museum_collection_types` - Get comprehensive list of object types available in each museum (based on systematic collection sampling) - `check_museum_has_object_type` - Check if a specific museum has objects of a particular type (e.g., paintings, sculptures) ### Information & Context - `get_smithsonian_units` - List all museums - `get_collection_statistics` - Collection metrics with per-museum breakdowns - `get_search_context` - Get search results as context data - `get_object_context` - Get detailed object information as context - `get_units_context` - Get list of units as context data - `get_stats_context` - Get collection statistics as context (includes sampling-based estimates) - `get_on_view_context` - Get currently exhibited objects as context ## Use Cases ### Research & Education - **Scholarly Research**: Multi-step academic investigation - **Lesson Planning**: Educational content creation - **Object Analysis**: In-depth cultural object study - **URL Retrieval**: Get validated object web page URLs (with anti-guessing protection) ### Curation & Exhibition - **Exhibition Planning**: Thematic object selection and visitor planning - **Visit Planning**: Find what's currently on display before visiting - **Exhibition Research**: Study current exhibition trends and displays - **Collection Development**: Gap analysis and acquisition - **Digital Humanities**: Large-scale analysis projects ### Development - **Cultural Apps**: Applications using museum data - **Educational Tools**: Interactive learning platforms - **API Integration**: Professional development workflows ## Requirements ### For npm/npx installation: - Node.js 16.0 or higher - Python 3.10 or higher (auto-detected and dependencies managed) - API key from [api.data.gov](https://api.data.gov/signup/) (free) - Internet connection for API access ### For Python installation: - Python 3.10 or higher - API key from [api.data.gov](https://api.data.gov/signup/) (free) - Internet connection for API access ## Testing ### Using npm/npx: ```bash # Test API connection smithsonian-mcp --test # Run MCP server smithsonian-mcp # Show help smithsonian-mcp --help ``` ### Using Python: ```bash # Test API connection python examples/test-api-connection.py # Run MCP server python -m smithsonian_mcp.server # Run test suite pytest tests/ # Run on-view functionality tests pytest tests/test_on_view.py -v # Run basic tests pytest tests/test_basic.py -v # Verify complete setup python scripts/verify-setup.py # VS Code Tasks (if using workspace) # - Test MCP Server # - Run Tests # - Format Code # - Lint Code ``` ## Service Management ### Linux (systemd) ```bash # Start service systemctl --user start smithsonian-mcp # Stop service systemctl --user stop smithsonian-mcp # Check status systemctl --user status smithsonian-mcp # Enable on boot systemctl --user enable smithsonian-mcp ``` ### macOS (launchd) ```bash # Load service launchctl load ~/Library/LaunchAgents/com.smithsonian.mcp.plist # Unload service launchctl unload ~/Library/LaunchAgents/com.smithsonian.mcp.plist # Check status launchctl list | grep com.smithsonian.mcp ``` ### Windows ```powershell # Start service Start-Service SmithsonianMCP # Stop service Stop-Service SmithsonianMCP # Check status Get-Service SmithsonianMCP ``` ## Troubleshooting For detailed troubleshooting guidance, including: - Common setup issues - Service startup problems - API key validation - Claude Desktop connection issues - Module import errors - Platform-specific problems Please refer to [TROUBLESHOOTING.md](TROUBLESHOOTING.md). ## Documentation ### Available Documentation - **[README.md](README.md)** - Main setup and usage guide (this file) - **[TROUBLESHOOTING.md](TROUBLESHOOTING.md)** - Comprehensive troubleshooting and common issues - **Examples** - Real-world usage scenarios in `examples/` directory - **Scripts** - Setup and utility scripts in `scripts/` directory ### Key Reference - **API Reference**: Complete tool and resource documentation in this README - **Deployment Guide**: Production deployment options included in setup instructions - **Integration Guide**: Claude Desktop and mcpo setup instructions in this README ## Contributing 1. Fork the repository 2. Create a feature branch 3. Make your changes 4. Run tests 5. Submit a pull request ## License MIT License - see LICENSE file for details. ## Acknowledgments - **Smithsonian Institution** for Open Access collections - **api.data.gov** for API infrastructure - **FastMCP** team for the MCP framework - **Model Context Protocol** community