MCPs with AutoGen Agents (with any Local LLM Model )

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AI Summary

Summary of the Anthropomorphic Model Context Protocol (MCP) Video

Overview of MCP

• MCP is a client-server architecture designed to connect generative AI models (LLMs) with external tools and data sources.
• It aims to provide a standardized protocol for AI applications to integrate with various tools.
• The protocol is likened to the USB-C standard for AI applications, offering pre-built integrations and the flexibility to switch between LLM providers.

Core Architecture

• Host: The user-facing application (e.g., cloud desktop apps, IDEs like Cursor and Wior).
• Client: Maintains a connection with the server and runs inside the host application.
• Server: Contains the core logic, tools, contexts, and resources.
• Communication is via a transport layer protocol, supporting either standard IO (STDIO) for local processes or Server-Sent Events (SSE) over HTTP for remote servers.
• All transport protocols use JSON-RPC 2.0 for message exchange.

Integration Example with Autogen Agent

• Install dependencies: Autogen agent chat API and Autogen extension package with MCP support.
• Integrate with MCP Fetch Server using UV tool (UV tool install mcp-server-fetch).
• Ensure the MCP server is available in the path (UV tool update shell).
• Import model client and helper tools from the Autogen extension package.
• Define parameters for connecting to the MCP server using the uvx command.
• Use the mcp_server_tools helper to create a client session, connect to the server, and list available tools.
• Tools are wrapped into an Autogen BaseTool class, providing the JSON structure for the tool schema.
• Pass the tool representation to the assistant agent, which can then execute function calls and summarize content.

Limitations of MCP

• The protocol is still in early stages and lacks examples for integration into arbitrary Python LLM applications without a framework.
• The setup process is complex, requiring system configuration and bundling executables with tools like UV or NPX.
• Many third-party MCPs are broken or don’t work well, reminiscent of the failed OpenAI GPT store project.
• Concerns about the developer experience and the practicality of a marketplace for reusable MCPs.

Conclusion

• The video covers the MCP protocol, integration with an Autogen agent, and current limitations.
• The presenter expresses hope for the project’s success but remains cautious due to early-stage development and challenges faced by similar initiatives.

(Note: No detailed instructions such as CLI commands, website URLs, or tips were provided in the text for extraction.)