LLM/MCP Personal Assistant

Overview

The LLM/MCP Personal Assistant is a ready‑to‑run application that bridges large language models (LLMs) with real‑world tools through the Model Context Protocol. It solves a common pain point for developers: building an AI‑powered assistant that can query external APIs, fetch data, and maintain conversational context without re‑implementing the protocol each time. By exposing a standard MCP interface, the server lets any Claude or Anthropic model—or future LLMs that support MCP—interact seamlessly with the assistant’s tooling layer.

At its core, the server offers a tool‑oriented architecture. The MCP implementation handles context serialization, tool invocation, and resource access, while the Express API layer manages user sessions and forwards requests from a modern React client. The result is a clean separation of concerns: the front end focuses on user experience, the API layer handles authentication and persistence, and the MCP server guarantees that the LLM can call tools like Wikipedia search or GitHub queries as if they were native functions. Developers can extend the tool set by adding new MCP resources, making it straightforward to plug in services such as weather APIs, calendar integrations, or custom data stores.

Key capabilities include:

• Context‑aware tool execution: The MCP server tracks conversational state and passes relevant context to each tool call, ensuring that the LLM’s responses are grounded in previous interactions.
• Resource abstraction: Tools such as Wikipedia and GitHub search are exposed through a uniform interface, allowing the model to request information without needing to understand API details.
• Session persistence: The Express backend maintains user sessions, so long‑running conversations can be resumed or analyzed later.
• Extensibility: Adding a new tool involves defining an MCP resource and registering it in the server; no changes to the client or LLM prompt are required.

Typical use cases include:

• Developer support: A programmer can ask the assistant to fetch documentation or code snippets from GitHub, and the LLM will return a concise summary.
• Research assistant: Users can query Wikipedia or other knowledge bases, and the model will synthesize findings into a coherent answer.
• Productivity workflow: By integrating additional tools (calendar, email), the assistant can schedule meetings or draft replies on behalf of the user.

In practice, a developer incorporates this server into an AI workflow by pointing their Claude or Anthropic client at the MCP endpoint, enabling tool calls without custom prompt engineering. The server’s modular design means that new capabilities can be rolled out incrementally, keeping the assistant up‑to‑date with emerging APIs and data sources.