Source Manager MCP Server

Overview

The Sqlite Literature Management Fastmcp MCP Server is a lightweight, yet powerful, solution for organizing and enriching scholarly and general literature within an AI‑driven workflow. It tackles the common pain point of scattered references by providing a unified, queryable store that maps each source—whether a paper, book, webpage, video or blog—to a persistent UUID. This identity system guarantees that duplicate entries are avoided and that references can be reliably linked across disparate tools and memory graphs.

At its core, the server exposes a set of intuitive MCP tools that let developers add sources, attach multiple identifiers (arXiv IDs, DOIs, Semantic Scholar IDs, ISBNs, URLs), and annotate them with structured notes. The schema supports status tracking (unread, reading, completed, archived) so that users can maintain a clear view of their research pipeline. By integrating with the MCP Memory Server, all entities and relationships are stored in a graph‑oriented memory backend, allowing AI assistants to retrieve contextual knowledge about how a particular source relates to concepts in the user’s personal knowledge graph.

Key capabilities include:

• Universal Source Identification – Each source is assigned a UUID and can carry an arbitrary number of external identifiers, ensuring robust cross‑reference between tools.
• Rich Note Management – Notes are stored with titles, timestamps and content, enabling AI assistants to surface relevant excerpts or summarize key points on demand.
• Entity Linking – Sources can be connected to graph entities with customizable relation types such as introduces, extends, or evaluates. This bi‑directional link makes it trivial for an assistant to answer questions like “Which papers introduce transformers?” or “What blogs discuss the same concept as a given entity?”
• Status Tracking – The status field allows developers to build workflows that automatically move items through stages of reading, annotating and archiving.

Real‑world use cases are abundant. A research team can maintain a shared literature database that automatically syncs with their AI assistant; the assistant can fetch the latest papers on a topic, suggest related videos, or pull in relevant blog posts. A developer building a knowledge‑base powered chatbot can rely on the server to keep track of sources that support specific answers, ensuring transparency and traceability. In an academic setting, students can annotate readings directly through the MCP interface, letting tutors or AI mentors retrieve those notes when reviewing progress.

Integration is seamless: developers invoke the server’s tools via MCP calls from any client (Claude, Gemini, or custom agents). Because the data resides in SQLite and is exposed through a memory graph, latency remains low even for large collections. The server’s design also makes it trivial to extend—additional source types or relation categories can be added by updating the schema, without touching client code. This combination of ease of use, robust data modeling and tight AI integration gives the Sqlite Literature Management Fastmcp MCP Server a distinct advantage for anyone looking to ground AI interactions in a well‑structured, searchable literature base.