MCP Research Assistant

MCP‑Server Research Tool – Overview

The MCP‑Server Research Tool is a long‑form research assistant built on top of the Model Context Protocol (MCP). It bridges an AI assistant—such as Claude—to a wide array of external data sources, enabling the generation of comprehensive, citation‑rich markdown documents. By querying primary materials (academic PDFs, textbooks), multimedia content (podcasts, YouTube videos), and encyclopedic references (Wikipedia), the server simulates a deep research workflow that would normally require manual browsing and note‑taking.

Developers benefit from this server because it abstracts the complexity of multi‑source aggregation and structured output. Instead of writing bespoke web scrapers or parsing PDFs, an AI client can simply invoke the MCP’s “research” resource. The server handles API key management, rate‑limit handling, and content extraction, returning a single, well‑formatted markdown file that contains both a detailed topic summary and an exhaustive source list. This makes it ideal for building knowledge‑base generators, study aids, or documentation pipelines that require up‑to‑date, verifiable information.

Key capabilities include:

• Multi‑source ingestion: Simultaneous access to text documents, video transcripts, and web articles.
• Structured markdown output: Automatic generation of a clean summary plus a reference section, ready for Markdown‑friendly editors like Obsidian.
• Long‑duration research: Configurable timeouts (e.g., ) allow the LLM to browse and synthesize information over extended periods, mirroring a human researcher’s depth.
• API‑key abstraction: The server accepts any compatible key (OpenRouter, OpenAI, Anthropic) via environment variables, simplifying credential management.
• CLI integration: A lightweight shell script () orchestrates the entire process, making it trivial to launch from a terminal or automation tool.

Typical use cases span academic research assistants that produce literature reviews, curriculum developers compiling study guides, or content creators generating in‑depth blog posts. In an AI workflow, a developer can register the MCP server with their assistant, then call the tool to fetch a topic summary on demand. The assistant can embed the resulting markdown directly into conversation threads, document repositories, or learning management systems.

What sets this server apart is its focus on real‑world data fidelity. By pulling from primary sources and providing a transparent source list, it mitigates hallucination risks common in LLM outputs. Its modular design—exposing resources, tools, and prompts through MCP—allows easy extension or replacement of data backends without touching the AI client. For developers building knowledge‑centric applications, this MCP server offers a ready‑made, scalable solution that turns an AI assistant into a fully fledged research companion.