MCP LLM

A comprehensive Dart package for integrating Large Language Models (LLMs) with the Model Context Protocol. Provides multi-provider LLM access, MCP client/server orchestration, and Contract Layer adapters for the mcp_bundle ecosystem.

Features

LLM Providers

Text/chat:

• Claude (Anthropic)
• OpenAI
• Gemini (Google)
• Vertex AI
• Bedrock (AWS)
• Cohere
• Mistral
• Groq
• Together
• Custom (build your own)

Cloud capability providers:

• Vision — Google Cloud Vision, OpenAI GPT-4 Vision
• ASR — OpenAI Whisper, Google Cloud Speech-to-Text
• OCR — Google Cloud Vision OCR, AWS Textract
• Binary storage — AWS S3, Google Cloud Storage

MCP Integration

• Multi-client and multi-server management
• Service routing, balancing, and pooling
• Every MCP revision the connected mcp_client / mcp_server speaks (2024-11-05 through 2026-07-28); MCP values are the protocol’s generated types (package:mcp_llm/mcp_types.dart)
• Authorization through the MCP client (McpOAuth in mcp_client)
• Typed MCP layer: every listing page, tool results rendered by content kind, sampling answered by a provider
• Tool calls run with the arguments the model gave, each keeping one id from the model’s turn to its result
• Images and stop sequences sent in each provider’s API format
• Per-peer records: declared capabilities, health (ping for a client, the server’s health record for a server), lifecycle state
• Deferred Tool Loading (60–80% token reduction)
• Multi-round tool calling
• Resource Tool Bridge (mcp_read_resource, mcp_list_resources)

Contract Layer Adapters

Implement mcp_bundle ports so mcp_skill, mcp_profile, mcp_knowledge, and mcp_knowledge_ops can plug mcp_llm providers in directly:

• LlmPortAdapter
• AsrPortAdapter
• OcrPortAdapter
• VisionPortAdapter
• StoragePortAdapter

Other

• Plugin system (custom tools and prompt templates)
• RAG with document store and vector search (Pinecone, Weaviate)
• Parallel processing and aggregation
• Unified logging via Dart logging package

Quick Start

import 'package:mcp_llm/mcp_llm.dart';

Future main() async {
  final provider = ClaudeProvider(
    apiKey: 'your-api-key',
    model: 'claude-sonnet-5-5',
    config: LlmConfiguration(apiKey: 'your-api-key', model: 'claude-sonnet-5-5'),
  );

  final response = await provider.complete(LlmRequest(prompt: 'Hello!'));
  print(response.text);
}

Through the registry, by provider name:

final mcpLlm = McpLlm()..registerProvider('claude', ClaudeProviderFactory());
final client = await mcpLlm.createClient(
  providerName: 'claude',
  config: LlmConfiguration(apiKey: 'your-api-key', model: 'claude-sonnet-5-5'),
);
final reply = await client.chat('Hello!');
print(reply.text);

Using an MCP Server’s Tools

Hand LlmClient a connected mcp_client; chat(enableTools: true) offers the