Tavily Server – A Simple Notes MCP

The Tavily Server is a lightweight Model Context Protocol (MCP) implementation written in TypeScript that turns an AI assistant into a collaborative note‑taking tool. By exposing notes as first‑class resources, it lets users create, retrieve, and summarize textual content without leaving the assistant’s environment. This solves a common pain point for developers who need to persist user data across sessions while still leveraging the power of LLMs.

At its core, the server manages a collection of plain‑text notes. Each note is identified by a URI and carries metadata such as title, creation time, and tags. The MCP resource interface allows the assistant to list all notes, fetch a specific note’s content, or query notes by metadata. Because the content is served as a simple MIME type (), it can be embedded directly into prompts or streamed back to the user in a readable format.

Creating new notes is handled through a dedicated tool called . The tool accepts a title and content, validates them, and stores the note in the server’s internal state. This operation is exposed to the assistant as a callable action, enabling workflows where the user can simply say “Create a note about tomorrow’s meeting” and have it persist for later reference. The tool’s design demonstrates how MCP tools can encapsulate state‑changing logic while keeping the interface declarative.

Summarization is another key feature. The prompt gathers all stored notes, embeds each as a resource reference, and returns a structured prompt that the assistant can pass to an LLM. This allows users to request high‑level overviews of their entire note collection, such as “Give me a summary of all notes from this week.” The server’s prompt format ensures that the LLM has direct access to each note’s content, improving accuracy and coherence in the generated summary.

In practice, developers can integrate Tavily Server into any AI workflow that benefits from persistent text storage. For example, a project management assistant can let users jot down action items, then later ask for a consolidated list of tasks. A research assistant could store literature snippets and generate summaries to aid literature reviews. Because the server communicates over stdio, it can be launched as a separate process and easily hooked into existing MCP‑enabled tools like Claude Desktop.

Unique advantages of Tavily Server include its minimal footprint, clear separation between resources and tools, and the ability to embed note content directly into prompts. This design pattern showcases how MCP servers can extend an assistant’s capabilities without requiring complex external databases or APIs, making it a practical choice for developers building context‑aware applications.