ai_sdk_mcp 1.2.0

MCP (Model Context Protocol) client for AI SDK Dart. Connect to MCP servers via SSE or stdio, discover tools, and use them with any LLM provider.

ai_sdk_mcp

Model Context Protocol (MCP) client for AI SDK Dart. Connects to MCP servers over HTTP SSE or stdio and exposes their tools as a typed ToolSet.

Installation

dependencies:
  ai_sdk_dart: ^1.1.0
  ai_sdk_mcp: ^1.1.0

Usage

SSE transport (remote server)

SseClientTransport speaks the MCP HTTP+SSE transport (protocol 2024-11-05): it opens a streaming GET to the SSE endpoint, POSTs requests to the endpoint the server advertises, and receives responses and server-pushed notifications (including resources/updated) over the event stream. It uses only web-safe HTTP, so it works on Flutter web. For a server that exposes a single plain JSON-RPC endpoint without SSE, use HttpClientTransport instead.

import 'package:ai_sdk_dart/ai_sdk_dart.dart';
import 'package:ai_sdk_mcp/ai_sdk_mcp.dart';
import 'package:ai_sdk_openai/ai_sdk_openai.dart';

final transport = SseClientTransport(
  url: Uri.parse('http://localhost:3000/sse'),
);

final client = MCPClient(transport: transport);
await client.initialize();

// Discover tools and use them in a generateText call
final tools = await client.tools();

final result = await generateText(
  model: openai('gpt-4.1-mini'),
  prompt: 'Search for "Dart programming"',
  tools: tools,
  maxSteps: 3,
);
print(result.text);

await client.close();

Stdio transport (local process)

Desktop/CLI only. StdioMCPTransport spawns an OS process and is not available on Flutter web — referencing it still compiles for web (via a stub), but constructing and using it on web throws UnsupportedError.

final transport = StdioMCPTransport(
  command: 'npx',
  args: ['-y', '@modelcontextprotocol/server-filesystem', '/tmp'],
);

final client = MCPClient(transport: transport);
await client.initialize();

final tools = await client.tools();
// Use tools with any generateText / streamText call...

await client.close();

Direct tool invocation

final result = await client.callTool('readFile', {'path': '/tmp/hello.txt'});
print(result); // file contents as string

Error handling

try {
  await client.callTool('dangerousOp', {});
} on MCPException catch (e) {
  print('MCP server error: ${e.message}');
}

License

MIT