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.

• 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

1. Create a directory for the application and navigate to it:

mkdir tanzu-platform-chat
cd tanzu-platform-chat

2. Download the latest JAR file and manifest.yml from the Releases page into this directory

3. Push the application to Cloud Foundry from the directory containing the downloaded files:

cf push

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.

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

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 for an SSE-based MCP server using the mcpSseURL tag:

cf cups mcp-server-sse -p '{"uri":"https://your-sse-mcp-server.example.com"}' -t "mcpSseURL"

2. Bind the MCP service to your application:

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

1. Create a user-provided service for a Streamable HTTP-based MCP server using the mcpStreamableURL tag:

cf cups mcp-server-streamable -p '{"uri":"https://your-streamable-mcp-server.example.com"}' -t "mcpStreamableURL"

2. Bind the MCP service to your application:

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

3. Restart your application to apply the bindings:

cf restart ai-tool-chat

Your chatbot will now register with the MCP agents, and the LLM will be able to invoke the agents' capabilities when responding to chat requests. The application supports both SSE and Streamable HTTP protocols simultaneously.

Agent2Agent (A2A) is a protocol that enables communication between independent AI agent systems. Unlike MCP servers which provide tools that your LLM can invoke, A2A agents are independent AI systems that can process messages and return their own intelligent responses. Think of MCP as giving your chatbot tools to use, while A2A lets your chatbot consult with other specialized AI agents.

• MCP Servers: Provide tools and data sources that your LLM invokes as part of generating a response
• A2A Agents: Independent AI agents that you can send messages to directly and receive complete responses from

1. Create a user-provided service for an A2A agent using the a2a tag:

cf cups a2a-agent -p '{"uri":"https://your-a2a-agent.example.com/.well-known/agent.json"}' -t "a2a"

The URI should point to the agent's Agent Card (a JSON descriptor at /.well-known/agent.json).

2. Bind the A2A service to your application:

cf bind-service ai-tool-chat a2a-agent

3. Restart your application to apply the binding:

cf restart ai-tool-chat

After binding A2A agents:

1. Click the Agents button (🤖) on the navigation rail
2. The Agents panel shows all connected A2A agents with their:
   • Name, description, and version
   • Health status (healthy/unhealthy)
   • Capabilities (streaming, push notifications, state history)
3. Click "Send Message" on any healthy agent
4. Type your message in the dialog and click "Send"
5. The agent's response appears in the chat with a distinct visual style

Agent messages are displayed with:

• A robot icon (🤖) and agent name header
• Tertiary color scheme (different from your LLM's responses)
• Clear attribution showing which agent responded

You can bind multiple A2A agents simultaneously, each providing specialized capabilities. The Agents panel displays health status for each agent, and the navigation button shows a status indicator (green/orange/red) based on overall agent health.

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