Convex MCP Server

Convex MCP Server – A Lightweight Notes Engine for AI Assistants

The Convex MCP Server addresses a common developer need: providing an easily‑scalable, AI‑friendly data store that can be queried and modified directly from an assistant like Claude. Instead of building custom APIs or persisting data in a full database, this server exposes a simple notes system over the Model Context Protocol. It allows assistants to read and create short text entries using uniform URIs, making the data immediately available in a structured format that can be referenced or passed to other tools.

At its core, the server implements two MCP concepts. First, resources represent individual notes; each note has a unique URI (), a title, content, and optional metadata. Resources are served with the plain‑text MIME type, ensuring that any client can fetch and display them without additional parsing. Second, tools expose a command that accepts a title and content, persisting the new entry in the server’s internal state. This separation of read (resources) and write (tools) operations mirrors typical CRUD patterns, but the simplicity of the API means developers can quickly prototype and iterate without boilerplate.

Key capabilities include:

• URI‑based access: Notes are reachable via predictable links, enabling cross‑assistant references or embedding in documents.
• Metadata support: Each note can carry arbitrary key/value pairs, useful for tagging or categorizing entries.
• Tool‑driven creation: The tool integrates seamlessly with AI workflows, allowing a user to ask the assistant to “add a note about tomorrow’s meeting” and have it stored instantly.
• Stateful persistence: The server keeps notes in memory across sessions, making it ideal for lightweight use cases where durability is not critical.

Typical use cases span from personal productivity to collaborative workflows. Developers can build a note‑taking sidebar in Claude Desktop that pulls notes from this server, or they can chain the tool with other AI‑generated content (e.g., summarizing meeting transcripts). In research settings, the server can serve as a quick repository for experiment logs that assistants can reference in real time. Because it’s written in TypeScript and follows standard MCP patterns, the Convex MCP Server is trivial to extend—adding new fields or additional tools requires only a few lines of code.

Integrating the server into an AI pipeline is straightforward: configure the MCP client (e.g., Claude Desktop) to point to the server’s executable, then use the assistant’s built‑in resource browsing or tool invocation features. The server’s plain‑text output ensures compatibility with any downstream processing, while the URI scheme guarantees that references remain resolvable across different sessions or assistants. In short, Convex MCP Server offers a minimal yet powerful bridge between an AI assistant and persistent text data, enabling developers to focus on higher‑level logic rather than plumbing.