DuckDuckGo Web Search MCP Server

The duckduckgo-web-search MCP server is a lightweight, TypeScript‑based tool that showcases the core concepts of the Model Context Protocol while providing developers with a simple yet powerful notes system. By exposing resources, tools, and prompts through MCP, it enables AI assistants—such as Claude—to create, manage, and summarize textual notes directly within an interactive workflow. This server addresses the common need for persistent, contextual information that can be referenced and updated on demand during AI conversations.

At its core, the server implements a resource model where each note is identified by a URI. These resources carry a title, body text, and optional metadata, all served as plain‑text MIME types for maximum compatibility. Developers can query the list of notes or retrieve a specific note by its URI, allowing AI assistants to read and incorporate stored information into responses. The simplicity of the resource format also means that any downstream tool or LLM can consume it without additional parsing logic.

The server offers a single tool—. This operation accepts a title and content, validates the input, and persists the new note in server state. By exposing this tool through MCP, an AI assistant can trigger note creation via natural language commands (e.g., “Create a note titled Project Plan with the following details …”), thereby turning conversational input into structured data that can be referenced later.

To leverage the accumulated notes, the server provides a prompt called . When invoked, it aggregates all stored notes and embeds them as resources within the prompt. The resulting structured prompt can be fed to any LLM, enabling a concise summary of all notes without the assistant having to fetch each note individually. This pattern demonstrates how MCP prompts can orchestrate complex data flows between tools and LLMs.

In practice, this server is ideal for developers building knowledge‑base assistants, personal productivity bots, or any application where an AI needs to persist and summarize user‑generated text. By integrating seamlessly with existing MCP clients, it allows developers to extend AI capabilities without reinventing storage or summarization logic. The clear separation of resources, tools, and prompts also makes it straightforward to evolve the system—adding new note fields, search capabilities, or richer summarization models—while keeping the interface stable for AI assistants.