This PR adds a proof-of-concept MCP client implementation allowing Symfony AI agents to connect to remote MCP servers and use their tools.

⚠️ This is a POC - The implementation is functional but not optimized for production use. It serves as a testing ground while waiting for the official PHP SDK to implement its client component (currently on their roadmap). Once available, this implementation should be migrated to use it.

How it works

1. Transports

Three transport implementations for different MCP server types:

2. McpToolbox

Adapts MCP tools to Symfony AI's ToolboxInterface:

• Converts MCP tool definitions to Tool objects
• Executes tools via McpClient::callTool()
• Converts MCP content types to Platform types:

3. ChainToolbox

Combines multiple ToolboxInterface into one, enabling agents to use tools from different sources (local + multiple MCPs).

4. ToolCallMessage changes

ToolCallMessage now supports multiple content types (not just string):

// Before
new ToolCallMessage($toolCall, 'text result');

// After
new ToolCallMessage($toolCall, new Text('...'), new Image($data, 'image/png'));

This enables MCP tools to return images/audio that get displayed in chat.

5. Bundle Integration

ai:
    mcp:
        my_mcp:
            transport: sse  # or http, stdio
            url: 'https://example.com/mcp/sse'
            tools:
                - 'tool_name'  # Optional: filter exposed tools

Creates services:

• ai.mcp.client.{name} - The MCP client
• ai.mcp.toolbox.{name} - The toolbox (use this in agent config)

Use in agent:

ai:
    agent:
        my_agent:
            tools:
                - 'ai.mcp.toolbox.my_mcp'

Breaking Changes

This PR introduces breaking changes to enable multimodal tool results:

1. ToolCallMessage::getContent() return type changed

// Before
public function getContent(): string

// After
public function getContent(): array // ContentInterface[]

Migration: Use $message->asText() to get the text content as a string.

2. ToolResultConverter::convert() return type changed

// Before
public function convert(ToolResult $toolResult): ?string

// After
public function convert(ToolResult $toolResult): array // ContentInterface[]

3. ToolCallMessage constructor signature changed

// Before
new ToolCallMessage($toolCall, 'content string')

// After (variadic)
new ToolCallMessage($toolCall, 'string', $image, $audio, ...)
new ToolCallMessage($toolCall, new Text('...'), new Image(...))

Passing a single string still works but internally converts to Text content.

Demo

Timeline bot combining two MCP servers:

1. Live City MCP → fetches news for a city
2. Graphify → generates a timeline diagram from the news

User asks about a city → Agent fetches news → Agent generates timeline → Image displayed in chat.

ai:
    mcp:
        graphify:
            transport: sse
            url: 'https://agents-mcp-hackathon-graphify.hf.space/gradio_api/mcp/sse'
            tools:
                - 'Graphify_generate_timeline_diagram'
        city:
            transport: sse
            url: 'https://kingabzpro-live-city-mcp.hf.space/gradio_api/mcp/sse'
            tools:
                - 'live_city_mcp_get_city_news'
    agent:
        timeline:
            platform: 'ai.platform.openai'
            model: 'gpt-4o-mini'
            prompt: |
                You are a news timeline generator. When the user asks about a city:
                1) First use live_city_mcp_get_city_news to fetch news for that city
                2) Then use Graphify_generate_timeline_diagram with this JSON format:
                {"title": "News from [City]", "events_per_row": 3, "events": [{"id": "1", "label": "Short title", "date": "2024-12-13"}]}
            tools:
                - 'ai.mcp.toolbox.graphify'
                - 'ai.mcp.toolbox.city'