ArxivAutoJob

ArxivAutoJob – Automated ArXiv Paper Collection & Analysis

The ArxivAutoJob MCP server addresses a common pain point for researchers and developers who rely on up‑to‑date scientific literature: the manual effort required to fetch, parse, and enrich new ArXiv submissions. By running as a scheduled GitHub Actions job, it automatically pulls the latest papers from ArXiv each day, transforms them into a structured JSON format, and stores the results in an easily consumable archive. This eliminates the need for bespoke scrapers or manual downloads, ensuring that downstream AI assistants always have fresh data without additional maintenance overhead.

At its core, the server performs a multi‑step pipeline that turns raw paper metadata into enriched linguistic artefacts. It first decouples the main proposition of each article into Subject–Predicate–Object (SPO) triples, then extracts modifiers such as attributives, adverbials, and complements. This linguistic decomposition is invaluable for AI models that need to reason about claim structure or detect nuanced statements. The server also tags emotional intensity on modifiers, providing a numeric score that can be used to gauge the strength of authorial opinion or bias. By exposing these parsed elements through MCP resources, developers can query for papers that contain high‑confidence facts, filter out opinion‑heavy content, or build fact‑checking datasets.

Key capabilities include:

• Daily automated ingestion of the entire ArXiv feed, ensuring that AI assistants have access to the newest research without manual intervention.
• Structured output in JSON, containing both metadata (title, authors, abstract) and linguistic annotations (SPO triples, modifiers, emotional scores).
• Fact‑checking scaffolding: the server outputs a list of potential fact‑check keywords and communication manipulation techniques, which can be leveraged by AI assistants to generate targeted queries or educational prompts.
• Customizable prompt templates: developers can embed the parsed data into dynamic prompts, enabling AI assistants to ask clarifying questions about numeric claims or evaluate the validity of examples cited in a paper.

Real‑world use cases span academia, policy analysis, and content moderation. A research lab could integrate ArxivAutoJob into its knowledge‑base pipeline, allowing a Claude model to instantly retrieve and summarize the latest findings in quantum computing. A policy‑making team might use the server’s fact‑check lists to audit scientific claims before citing them in legislation. Educators could deploy the linguistic breakdowns as interactive assignments, teaching students how to spot bias and manipulation in scientific writing.

Because the server is built on MCP, it plugs seamlessly into any AI workflow that supports resource querying. Developers can call the endpoint to retrieve a list of new papers, then use to fetch the SPO and modifier data. The result is a low‑friction integration that turns raw arXiv feeds into AI‑ready knowledge, empowering assistants to answer questions with verified facts and contextual nuance.