Dock AI▌
by dock-ai
Dock AI: Resolve business domains to discover MCP endpoints for real-world entities quickly and securely.
Discover MCP endpoints for real-world entities by resolving business domains.
Both formats append explainx.ai attribution and the canonical URL for this MCP server listing.
best for
- / AI agents needing to interact with specific businesses
- / Discovering booking systems for restaurants and hotels
- / Finding MCP connectors for real-world services
capabilities
- / Resolve business domains to find available MCP connectors
- / Discover which services integrate with restaurants, hotels, and other businesses
- / Get MCP server endpoints for real-world entities
- / Retrieve business location and capability information
what it does
Discovers MCP servers that can interact with specific businesses by looking up their domains in a registry.
about
Dock AI is an official MCP server published by dock-ai that provides AI assistants with tools and capabilities via the Model Context Protocol. Dock AI: Resolve business domains to discover MCP endpoints for real-world entities quickly and securely. It is categorized under developer tools.
how to install
You can install Dock AI in your AI client of choice. Use the install panel on this page to get one-click setup for Cursor, Claude Desktop, VS Code, and other MCP-compatible clients. This server supports remote connections over HTTP, so no local installation is required.
license
MIT
Dock AI is released under the MIT license. This is a permissive open-source license, meaning you can freely use, modify, and distribute the software.
readme
Dock AI MCP
MCP server for Dock AI - discover MCP endpoints for real-world entities.
What is this?
Dock AI is a registry that maps businesses to their MCP connectors. This MCP server allows AI agents to discover which MCP servers can interact with a given entity (restaurant, hotel, salon, etc.) by querying the Dock AI registry.
Hosted Version
Use the hosted version at https://connect.dockai.co/mcp - no installation required.
{
"mcpServers": {
"dock-ai": {
"url": "https://connect.dockai.co/mcp"
}
}
}
Self-Hosting
Deploy to Vercel
Run locally
# Using uvx
uvx dock-ai-mcp
# Or install and run
pip install dock-ai-mcp
dock-ai-mcp
The server starts on http://0.0.0.0:8080/mcp.
Tools
resolve_domain
Check if an MCP connector exists for a business domain.
Input:
domain(string): The business domain to resolve (e.g., "example-restaurant.com")
Output:
{
"domain": "example-restaurant.com",
"entities": [
{
"name": "Example Restaurant",
"path": null,
"location": { "city": "Paris", "country": "FR" },
"mcps": [
{
"provider": "booking-provider",
"endpoint": "https://mcp.booking-provider.com",
"entity_id": "entity-123",
"capabilities": ["reservations", "availability"],
"verification": { "level": 2, "method": "dual_attestation" }
}
]
}
],
"claude_desktop_config": {
"mcpServers": {
"booking-provider": { "url": "https://mcp.booking-provider.com/mcp" }
}
}
}
Examples
Example 1: Restaurant Reservation
User: "Book a table at Gloria Osteria Paris"
Agent: [searches web for "Gloria Osteria Paris official website"]
-> Finds domain: gloria-osteria.com
[calls resolve_domain("gloria-osteria.com")]
-> Gets MCP endpoint for SevenRooms
-> Connects to the MCP server
-> Books the table
Example 2: Hotel Booking
User: "I need a room at The Hoxton in London"
Agent: [searches web for "The Hoxton London website"]
-> Finds domain: thehoxton.com
[calls resolve_domain("thehoxton.com")]
-> Gets MCP endpoints for available booking providers
-> Uses the MCP to check availability and book
Example 3: Business with No MCP Yet
User: "Book at Le Paris Paris restaurant"
Agent: [calls resolve_domain("leparisparis.fr")]
-> Response shows pending_providers: [{ "provider": "thefork", ... }]
-> Informs user: "This restaurant uses TheFork for reservations,
but TheFork hasn't published an MCP connector yet.
You can book directly on TheFork's website."
Support
- Documentation: dockai.co/docs
- Issues: GitHub Issues
- Email: [email protected]
Privacy
This MCP server queries the Dock AI registry API to resolve domains. No user data is collected or stored. See our Privacy Policy.
License
MIT
FAQ
- What is the Dock AI MCP server?
- Dock AI is a Model Context Protocol (MCP) server profile on explainx.ai. MCP lets AI hosts (e.g. Claude Desktop, Cursor) call tools and resources through a standard interface; this page summarizes categories, install hints, and community ratings.
- How do MCP servers relate to agent skills?
- Skills are reusable instruction packages (often SKILL.md); MCP servers expose live capabilities. Teams frequently combine both—skills for workflows, MCP for APIs and data. See explainx.ai/skills and explainx.ai/mcp-servers for parallel directories.
- How are reviews shown for Dock AI?
- This profile displays 75 aggregated ratings (sample rows for discoverability plus signed-in user reviews). Average score is about 4.8 out of 5—verify behavior in your own environment before production use.
Use Cases▌
Extended AI Capabilities
Add new capabilities to Claude beyond text generation
Example
Access external data sources, execute code, interact with tools and services
Transform Claude from chatbot to action-taking agent
Context Enhancement
Provide Claude with access to relevant context and data
Example
Load project documentation, access knowledge bases, query databases
Get more accurate, context-aware responses
Workflow Automation
Automate multi-step workflows combining AI and external tools
Example
Research → Summarize → Create document → Send notification
Complete complex tasks end-to-end without manual steps
Implementation Guide▌
Prerequisites
- ›Claude Desktop 0.7.0+ or Cursor IDE with MCP support
- ›Basic understanding of MCP architecture and capabilities
- ›Access credentials for integrated services (if required)
- ›Willingness to experiment and iterate on configuration
Time Estimate
15-60 minutes depending on server complexity
Installation Steps
- 1.Install MCP server: npm install -g [package-name] or via GitHub
- 2.Add server configuration to ~/.claude/mcp.json
- 3.Provide required credentials and configuration
- 4.Restart Claude Desktop to load new server
- 5.Test basic functionality with simple prompts
- 6.Explore capabilities and experiment with use cases
- 7.Document successful patterns for reuse
Troubleshooting
- ⚠MCP server not loading: Check config syntax, verify installation
- ⚠Connection errors: Check network, firewall, credentials
- ⚠Feature not working: Read server docs, check required parameters
- ⚠Performance issues: Monitor resource usage, check for network latency
- ⚠Conflicts with other servers: Check port assignments, namespace collisions
Best Practices▌
✓ Do
- +Read server documentation thoroughly before setup
- +Start with simple use cases to validate functionality
- +Test in non-production environment first
- +Monitor resource usage and performance
- +Keep servers updated for bug fixes and new features
- +Document configuration for team members
- +Use environment variables for sensitive configuration
✗ Don't
- −Don't grant overly permissive access to MCP servers
- −Don't skip reading security considerations in docs
- −Don't expose sensitive data without proper controls
- −Don't run untrusted MCP servers without code review
- −Don't ignore error messages—investigate root cause
💡 Pro Tips
- ★Combine multiple MCP servers for powerful workflows
- ★Create custom MCP servers for your specific needs
- ★Share successful configurations with team
- ★Use MCP inspector for debugging
- ★Join MCP community for tips and troubleshooting
Technical Details▌
Architecture
Model Context Protocol standardizes how AI hosts (Claude, Cursor) communicate with external tools and data sources through server implementations.
Protocols
- Model Context Protocol (MCP)
- JSON-RPC 2.0
- stdio or HTTP transport
Compatibility
- Claude Desktop
- Cursor IDE
- Custom MCP clients
When to Use This▌
✓ Use When
Use when you need Claude to access external data, execute actions, or integrate with tools. Best for extending AI capabilities beyond conversation.
✗ Avoid When
Avoid when native integrations exist (use official APIs directly), for real-time critical systems, or when security/compliance requires zero external dependencies.
Integration▌
- →Tool composition: Chain multiple MCP tools in workflows
- →Context augmentation: Provide AI with relevant external data
- →Action delegation: Let AI execute tasks on external systems
- →Bidirectional sync: Keep AI context and external systems in sync
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
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Ratings
4.8★★★★★75 reviews- ★★★★★Pratham Ware· Dec 28, 2024
According to our notes, Dock AI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Neel Mensah· Dec 24, 2024
Dock AI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Nikhil Bansal· Dec 24, 2024
Dock AI is a well-scoped MCP server in the explainx.ai directory — install snippets and categories matched our Claude Code setup.
- ★★★★★Dhruvi Jain· Dec 20, 2024
Dock AI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
- ★★★★★Neel Abebe· Dec 20, 2024
Dock AI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Mateo Menon· Dec 12, 2024
Dock AI has been reliable for tool-calling workflows; the MCP profile page is a good permalink for internal docs.
- ★★★★★Isabella Mehta· Dec 8, 2024
I recommend Dock AI for teams standardizing on MCP; the explainx.ai page compares cleanly with sibling servers.
- ★★★★★Amina Mensah· Dec 8, 2024
Strong directory entry: Dock AI surfaces stars and publisher context so we could sanity-check maintenance before adopting.
- ★★★★★Tariq Nasser· Dec 4, 2024
According to our notes, Dock AI benefits from clear Model Context Protocol framing — fewer ambiguous “AI plugin” claims.
- ★★★★★Harper Bansal· Nov 27, 2024
Dock AI reduced integration guesswork — categories and install configs on the listing matched the upstream repo.
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