Question 1

What is an MCP Server?

Accepted Answer

MCP (Model Context Protocol) is an open protocol standardizing how applications provide context to LLMs. An MCP server acts as a bridge, allowing AI models to access and interact with external data sources and tools, enhancing their understanding and enabling them to perform complex tasks.

Question 2

How does this MCP Server help in finding dog shelters?

Accepted Answer

This specific MCP server is designed to locate dog shelters within a specified radius of San Francisco. It utilizes an API to accept location and radius parameters, returning a list of relevant shelters, making it easy to integrate into applications needing location-based shelter information.

Question 3

What are the main use cases for this MCP Server?

Accepted Answer

Besides finding shelters, potential use cases include emergency response for pet displacement, integration into animal welfare organization websites, lost pet recovery systems, pet-friendly travel applications, training tools for animal care professionals, and hyperlocal marketing for pet-related businesses.

Question 4

How can I set up this MCP Server locally?

Accepted Answer

To set up locally, clone the repository, run `npm install` to install dependencies, copy `.env.example` to `.env` and set the API keys, and then run `npm start` to start the server.

Question 5

How can I set up this MCP Server using Docker?

Accepted Answer

To set up using Docker, first build the Docker image with: `docker build -t sf-dog-shelter-finder .` and then run the Docker container using: `docker run -p 3000:3000 sf-dog-shelter-finder`.

Question 6

What kind of API calls can I make to the MCP Server?

Accepted Answer

You can list available tools using a POST request to `/api/mcp` with the method `tools/list`. You can also call the shelter locator tool using a POST request with the method `tools/call`, including parameters for location and radius.

Question 7

How does this MCP Server integrate with the UBOS platform?

Accepted Answer

This server integrates by providing LLMs with contextual information about dog shelter locations, allowing UBOS to provide more relevant and accurate responses. It can be used within UBOS workflows for orchestration, customization, data integration, and agent building.

Question 8

Is the MCP Server scalable?

Accepted Answer

Yes, the server is designed to handle a high volume of requests, making it suitable for applications with growing user traffic.

Question 9

Can I customize the search radius for finding shelters?

Accepted Answer

Yes, the search radius can be adjusted to refine the results, allowing users to focus on shelters within a specific distance of their location.

Question 10

What if I want to add more details about the shelters, like contact information or operating hours?

Accepted Answer

While the current server may not explicitly provide these details, it can be extended to include more comprehensive information about each shelter, further enhancing its value to users.

Question 11

What is UBOS and how does this MCP server fit into its overall strategy?

Accepted Answer

UBOS is a Full-stack AI Agent Development Platform focused on bringing AI agents to every business department. The MCP server fits into UBOS strategy by providing specialized, easily integrable components that enrich AI agents with specific contextual data, in this case, location data for dog shelters, enabling more practical and useful AI applications.

How to run

Local Setup

Docker Setup

Example API Calls

San Francisco Dog Shelter Finder

Resources

Project Details

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