Reading Support MCP Server

Overview

The Reading Support MCP server is a lightweight, purpose‑built helper that extends the functionality of the ReadRecord application. It bridges the gap between an AI assistant and a structured reading‑management system, allowing the assistant to query, modify, and enrich reading records without exposing raw database access. This is particularly valuable for developers who want to embed intelligent reading analytics or recommendation features into chat‑based interfaces while keeping the underlying data layer secure and abstracted.

At its core, the server exposes a single executable command that listens for MCP messages. When invoked by an AI client, it interprets the request, performs the necessary lookup or update on ReadRecord’s storage layer, and returns a JSON payload that the assistant can consume. The simplicity of the interface—just a command name and a set of JSON‑structured arguments—means it can be dropped into existing MCP ecosystems with minimal friction. Developers can add the server to their configuration and start sending queries such as “list unread books” or “mark chapter 3 as read,” letting the assistant act as a natural‑language front end.

Key capabilities include:

• Record retrieval: Fetch book titles, authors, and progress metrics based on filters or search terms.
• Progress updates: Increment page counts, mark chapters as completed, and toggle reading status flags.
• Metadata enrichment: Attach tags or notes to records, enabling richer context for future queries.
• Statistical summaries: Provide aggregated data like total pages read or average reading speed, useful for dashboards.

These features are delivered through a clean JSON schema that maps directly to ReadRecord’s internal data structures, ensuring consistency and reducing the chance of malformed requests. Because the server runs as a separate process, it can be scaled independently or sandboxed behind authentication layers, giving teams fine control over access.

Real‑world use cases abound: a study‑group chatbot can keep track of collective progress, a personal reading assistant can suggest the next book based on completed titles, and an educational platform can generate progress reports for instructors. In each scenario the MCP server acts as a secure, low‑overhead conduit between human language and structured data.

Integration into AI workflows is straightforward. Once the server is registered in the MCP configuration, any Claude or other AI assistant that supports MCP can issue tool calls to . The assistant’s prompt templates can include conditional logic—e.g., “If the user wants to see their reading list, call with action ”—making the tool feel like a native part of the conversation. The server’s lightweight design also means it can run locally on a developer’s machine or in a containerized environment, fitting seamlessly into CI/CD pipelines or production deployments.