What is Couchbase Mcp

Couchbase-MCP is a Model Context Protocol server that enables AI models to interact with external tools and data sources, specifically providing semantic search capabilities for Star Wars planets using Couchbase’s vector search functionality.

Use cases

Use cases include retrieving detailed information about Star Wars planets, finding similar planets based on characteristics, and enhancing AI models with contextual data for improved user interactions.

How to use

To use Couchbase-MCP, integrate it with AI models that support the Model Context Protocol. You can call its tools to fetch planet information or find similar planets based on vector embeddings.

Key features

Key features include efficient vector search using Couchbase’s index, timeout protection for operations, connection management, comprehensive error handling, and full TypeScript implementation with type safety.

Where to use

Couchbase-MCP can be used in applications requiring semantic search capabilities, particularly in gaming, educational tools, or any AI-driven applications that involve data about fictional universes like Star Wars.

Couchbase Model Context Protocol Server

This project demonstrates the implementation of a Model Context Protocol (MCP) server that provides semantic search capabilities for Star Wars planets using Couchbase’s vector search functionality.

Overview

The Model Context Protocol (MCP) is a standardized way for AI models to interact with external tools and data sources. This implementation creates an MCP server that allows AI models to:

1. Fetch detailed information about Star Wars planets
2. Find similar planets based on vector embeddings

How It Works

Model Context Protocol Integration

The server implements two main MCP tools:

{
tools: [
{
name: "fetch_planet_name",
description: "Fetch a Star Wars planet by name",
inputSchema: // ... schema for planet name
},
{
name: "find_planets_which_are_similar",
description: "Find similar planets by name to the given name",
inputSchema: // ... schema for planet name
}
]
}

These tools can be discovered and called by AI models that support the Model Context Protocol.

Couchbase Vector Search

The implementation uses Couchbase’s vector search capabilities to find similar planets:

1. Each planet document in Couchbase includes an embedding field containing a vector representation of the planet’s characteristics
2. When searching for similar planets:
   • Retrieves the source planet’s embedding
   • Uses Couchbase’s vector search to find planets with similar embeddings
   • Returns the top 5 most similar planets

Key Features

• Efficient Vector Search: Utilizes Couchbase’s vector search index for fast similarity lookups
• Timeout Protection: Implements timeouts for both search and document fetching operations
• Connection Management: Properly manages Couchbase connections with cleanup
• Error Handling: Comprehensive error handling and debugging support
• Type Safety: Full TypeScript implementation with proper type definitions

Setup

Prerequisites

• Node.js
• Couchbase Server with vector search capability
• Environment variables:

  COUCHBASE_URL=
  COUCHBASE_USERNAME=
  COUCHBASE_PASSWORD=
  COUCHBASE_BUCKET=
  COUCHBASE_SCOPE=
  COUCHBASE_COLLECTION=
  

Data Structure

Each planet document should follow this structure:

interface StarWarsCharacter {
name: string;
rotation_period: string;
orbital_period: string;
diameter: string;
climate: string;
gravity: string;
terrain: string;
surface_water: string;
population: string;
residents: string[];
films: string[];
created: string;
edited: string;
url: string;
embedding?: number[]; // Vector embedding for similarity search
}

Vector Search Index

Create a vector search index in Couchbase named vector-search-index that indexes the embedding field.

Usage

1. Start the server:

   npm start
   

2. The server will listen for MCP requests via stdin/stdout.

3. AI models can interact with the server using these example queries: