What is Cf Mcp Client

cf-mcp-client is a client application designed to interact with the MCP server, facilitating communication and data exchange.

Use cases

Use cases include developing chatbots, real-time data analytics platforms, and applications requiring AI-driven insights.

How to use

To use cf-mcp-client, first install Angular CLI, set the required environment variables for API keys and passwords, then build the project using Maven and run the generated JAR file.

Key features

Key features include integration with Angular for a responsive user interface, support for OpenAI API, and secure password management.

Where to use

cf-mcp-client can be used in web applications that require real-time data processing and interaction with AI services.

Tanzu Platform Chat: AI Chat Client for Cloud Foundry

Overview

Tanzu Platform Chat (cf-mcp-client) is a Spring chatbot application that can be deployed to Cloud Foundry and consume platform AI services. It’s built with Spring AI and works with LLMs, Vector Databases, and Model Context Protocol Agents.

Prerequisites

• Java 21 or higher
  • e.g. using sdkman sdk install java 21.0.7-oracle
• Maven 3.8+
  • e.g. using sdkman sdk install maven
• Access to a Cloud Foundry Foundation with the GenAI tile or other LLM services
• Developer access to your Cloud Foundry environment

Deploying to Cloud Foundry

Preparing the Application

1. Build the application package:

mvn clean package

2. Push the application to Cloud Foundry:

cf push

Binding to Large Language Models (LLM’s)

1. Create a service instance that provides chat LLM capabilities:

cf create-service genai [plan-name] chat-llm

2. Bind the service to your application:

cf bind-service ai-tool-chat chat-llm

3. Restart your application to apply the binding:

cf restart ai-tool-chat

Now your chatbot will use the LLM to respond to chat requests.

Binding to Vector Databases

1. Create a service instance that provides embedding LLM capabilities

cf create-service genai [plan-name] embeddings-llm 

2. Create a Postgres service instance to use as a vector database

cf create-service postgres on-demand-postgres-db vector-db

3. Bind the services to your application

cf bind-service ai-tool-chat embeddings-llm 
cf bind-service ai-tool-chat vector-db

4. Restart your application to apply the binding:

cf restart ai-tool-chat

5. Click on the document tool on the right-side of the screen, and upload a .PDF File
   

Now your chatbot will respond to queries about the uploaded document

Binding to MCP Agents

Model Context Protocol (MCP) servers are lightweight programs that expose specific capabilities to AI models through a standardized interface. These servers act as bridges between LLMs and external tools, data sources, or services, allowing your AI application to perform actions like searching databases, accessing files, or calling external APIs without complex custom integrations.

1. Create a user-provided service that provides the URL for an existing MCP server:

cf cups mcp-server -p '{"mcpServiceURL":"https://your-mcp-server.example.com"}'

2. Bind the MCP service to your application:

cf bind-service ai-tool-chat mcp-server

3. Restart your application:

cf restart ai-tool-chat

Your chatbot will now register with the MCP agent, and the LLM will be able to invoke the agent’s capabilities when responding to chat requests.

Using a Vector Store for Conversation Memory

If you are bound to a vector database and an embedding model, then your chat memory will persist across application restarts and scaling.

1. Follow the instructions above in Binding to Vector Databases