Smithsonian Open Access MCP Server

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:

# 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:

chmod +x setup.sh
./setup.sh

Windows:

.\setup.ps1

Option 3: Manual Setup

1. Get API Key: api.data.gov/signup (free)
2. Install: pip install -r 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:

python scripts/verify-setup.py

Features

Core Functionality

• Search Collections: 3+ million objects across 19 Smithsonian museums
• Object Details: Complete metadata, descriptions, and provenance
• On-View Status: ⭐ NEW - Find objects currently on physical exhibit
• Image Access: High-resolution images (CC0 licensed when available)
• 3D Models: Interactive 3D content where available
• Museum Information: Browse all Smithsonian institutions

AI Integration

• 12 MCP Tools: Search, filter, retrieve collection data, check exhibition status, and get context
• Smart Context: Contextual data sources for AI assistants
• Rich Metadata: Complete object information and exhibition details
• Exhibition Planning: ⭐ NEW - Tools to find and explore currently exhibited objects

Integration

Claude Desktop

Option 1: Using npm/npx (Recommended)

1. Configure (claude_desktop_config.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):

{
  "mcpServers": {
    "smithsonian_open_access": {
      "command": "python",
      "args": ["-m", "smithsonian_mcp.server"],
      "env": {
        "SMITHSONIAN_API_KEY": "your_key_here"
      }
    }
  }
}

Or copy the provided claude-desktop-config.json and update the API key

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

# Install mcpo
pip install mcpo

# Or using uvx
uvx mcpo --help

Configuration

Create a mcpo-config.json file:

{
  "mcpServers": {
    "smithsonian_open_access": {
      "command": "python",
      "args": ["-m", "smithsonian_mcp.server"],
      "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

# Start mcpo with hot-reload
mcpo --config mcpo-config.json --port 8000 --hot-reload

# With API key authentication
mcpo --config 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:

[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 mcpo-config.json --port 8000
Restart=always
RestartSec=10

[Install]
WantedBy=multi-user.target

# Enable and start service
sudo systemctl enable mcpo
sudo systemctl start mcpo
sudo systemctl status mcpo

Troubleshooting mcpo

"ModuleNotFoundError: No module named 'smithsonian_mcp'" This occurs when mcpo can't find the Smithsonian MCP module. Fix by:

1. Use absolute Python path in your mcpo config:

{
  "command": "/full/path/to/your/project/.venv/bin/python",
  "env": {
    "PYTHONPATH": "/full/path/to/your/project"
  }
}

2. Verify paths:

# Check Python executable exists
ls -la /path/to/your/project/.venv/bin/python

# Test module import
/path/to/your/project/.venv/bin/python -c "import smithsonian_mcp; print('OK')"

3. Regenerate config with setup script:

./setup.sh  # Will create mcpo-config.json with correct paths

"Connection closed" errors

• Ensure API key is valid and set in environment
• Check that the virtual environment has all dependencies installed
• Verify the MCP server can start manually: python -m smithsonian_mcp.server --test

"Port 8000 already in use"

# Check what's using the port
lsof -i :8000
# Or use different port
mcpo --config mcpo-config.json --port 8001

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
• 3D Models: Interactive digital assets

MCP Tools

Search & Discovery

• search_collections - Advanced search with filters (now includes on_view parameter)
• get_object_details - Detailed object information
• search_by_unit - Museum-specific searches
• ⭐ get_objects_on_view - NEW - Find objects currently on physical exhibit
• ⭐ check_object_on_view - NEW - Check if a specific object is on display

Information & Context

• get_smithsonian_units - List all museums
• get_collection_statistics - Collection metrics
• 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
• ⭐ get_on_view_context - NEW - Get currently exhibited objects as context

New: On-View Functionality 🎨

What's New in Phase 1

The MCP server now includes comprehensive support for finding objects currently on physical exhibit at Smithsonian museums. This is a priority feature aligned with the Smithsonian's official API documentation.

Key Features

• Find Exhibited Objects: Search for objects currently on display
• Check Exhibition Status: Verify if specific objects are on view
• Filter by Museum: Find what's on display at specific Smithsonian units
• Exhibition Details: Access exhibition title and location information
• Combined Filters: Mix on-view status with other search criteria

Usage Examples

Find all objects currently on view:

# Ask Claude:
"What objects are currently on physical exhibit at the Smithsonian?"

# Or with filters:
"Show me paintings currently on display at the National Portrait Gallery"

Check if a specific object is on view:

# Ask Claude:
"Is object edanmdm-nmah_1234567 currently on display?"

Combine with other filters:

# Ask Claude:
"Find CC0 licensed objects currently on view with high-resolution images"

Tool Details

get_objects_on_view

Find objects currently on physical exhibit.

Parameters:

• unit_code (optional): Filter by Smithsonian unit (e.g., "NMNH", "NPG")
• limit: Maximum results (default: 20, max: 100)
• offset: Pagination offset

Returns: Search results containing objects currently on exhibit

check_object_on_view

Check if a specific object is currently on display.

Parameters:

• object_id: Unique identifier for the object

Returns: Object details including exhibition status

search_collections (enhanced)

Now includes on_view parameter for filtering.

New Parameter:

• on_view (boolean): Filter objects by exhibition status
  • True: Only objects currently on display
  • False: Only objects not on display
  • None: No filter (default)

Implementation Notes

This feature is based on the Smithsonian's onPhysicalExhibit metadata field, which indicates whether an object is currently accessible to the public in a physical exhibition. The implementation includes:

• Full API alignment with EDAN metadata model v1.09
• Fielded search support using onPhysicalExhibit:"Yes" queries
• Comprehensive test coverage (15 unit tests)
• Exhibition metadata extraction (title, location)

Use Cases

Research & Education

• Scholarly Research: Multi-step academic investigation
• Lesson Planning: Educational content creation
• Object Analysis: In-depth cultural object study

Curation & Exhibition

• Exhibition Planning: Thematic object selection and visitor planning
• Visit Planning: ⭐ NEW - Find what's currently on display before visiting
• Exhibition Research: ⭐ NEW - 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 (free)
• Internet connection for API access

For Python installation:

• Python 3.10 or higher
• API key from api.data.gov (free)
• Internet connection for API access

Testing

Using npm/npx:

# Test API connection
smithsonian-mcp --test

# Run MCP server
smithsonian-mcp

# Show help
smithsonian-mcp --help

Using Python:

# 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)

# 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)

# 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

# Start service
Start-Service SmithsonianMCP

# Stop service
Stop-Service SmithsonianMCP

# Check status
Get-Service SmithsonianMCP

Troubleshooting

Common Issues

"API key validation failed"

• Get a free key from api.data.gov/signup
• Ensure no extra spaces in your API key
• Check that .env file contains: SMITHSONIAN_API_KEY=your_key_here

"Service failed to start"

• Run python scripts/verify-setup.py for diagnostics
• Check logs: journalctl --user -u smithsonian-mcp (Linux) or ~/Library/Logs/com.smithsonian.mcp.log (macOS)
• Ensure virtual environment is activated

"Claude Desktop not connecting"

• Restart Claude Desktop after configuration
• Check Claude Desktop config file exists and contains correct paths
• Verify MCP server is running: python -m smithsonian_mcp.server

"Module import errors"

• Activate virtual environment: source .venv/bin/activate (Linux/macOS) or .\venv\Scripts\Activate.ps1 (Windows)
• Reinstall dependencies: pip install -r requirements.txt

Getting Help

1. Run verification script: python scripts/verify-setup.py
2. Check the Integration Guide
3. Review GitHub Issues

Documentation

• Integration Guide: Claude Desktop and VS Code setup
• API Reference: Complete tool and resource documentation
• Examples: Real-world usage scenarios
• Deployment Guide: Production deployment options

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