# Reading Project Manager MCP > Deterministic Reading Project Manager analyzes your reading backlog, calculating precise completion times based on your specific words per minute (WPM). This MCP uses a strict algorithmic engine to generate progress reports, track total percentage completed, and sequence books using the Snowball Method for maximum momentum. ## Overview - **Category:** productivity - **Price:** Free - **Tags:** task-management, time-estimation, project-tracking, algorithmic-sorting, backlog-management ## Description Dealing with huge backlogs—think research papers or technical documentation—is tough. Most AI clients struggle when you need hard math: they guess time-to-completion instead of calculating it based on pages and your reading speed. This MCP changes that. You feed the system your entire list, and our dedicated engine processes it through a fixed algorithm. It doesn't just count pages; it figures out exactly how many hours you have left based on your personal WPM. Beyond time estimates, it gives you full progress analytics in a clean dashboard—showing what percentage of the goal is done versus what’s still sitting in the pipeline. Plus, it can even tell you the best reading order right now to keep you motivated, prioritizing books that will get you across the finish line fastest. ## Tools ### analyze_reading_list Analyzes a reading list to generate progress reports, estimate completion times using WPM, and construct an optimized reading sequence via the Snowball Method. ## Prompt Examples **Prompt:** ``` Analyze my book queue and tell me how many hours I have left. ``` **Response:** ``` Using the analyze_reading_list tool: You have 1,200 pages left across 4 unread books. At 250 WPM, it will take approximately 24 hours of focused reading. ``` **Prompt:** ``` What book should I read next to build momentum? ``` **Response:** ``` Using the analyze_reading_list tool: Based on the Snowball sequence, you should finish 'The Lean Startup' (only 40 pages left), followed immediately by your shortest unread book, 'Who Moved My Cheese' (96 pages). ``` **Prompt:** ``` Calculate my progress across these 15 research papers. ``` **Response:** ``` Using the analyze_reading_list tool: Your diagnostic shows you are 45.2% complete overall, having finished 7 papers entirely and being halfway through another. ``` ## Capabilities ### Estimate time remaining Calculates exact hours left based on total unread pages and your set words per minute (WPM). ### Generate progress reports Creates a real-time JSON dashboard showing overall completion percentage, pages read vs. unread, and current pipeline status. ### Sequence reading material Automatically sorts your backlog using the Snowball Method to build rapid reading momentum by prioritizing short or near-complete items. ## Use Cases ### Wrapping up a semester-long literature review A PhD candidate has 15 research papers spread across different topics. Instead of manually checking off articles in an Excel sheet, they pass the list to the MCP. The agent uses `analyze_reading_list` and immediately gets a diagnostic report showing them exactly what percentage is complete and how many days of focused reading it will take. ### Prepping for a major documentation release A technical writer has a massive list of internal guides that need to be reviewed. They use the MCP to analyze the full stack, getting both a time estimate and an optimized sequence that forces them to read related short documents first, building momentum before tackling the longest manual. ### Client onboarding review cycle A consultant is tasked with reviewing 20 client-specific white papers. To manage their time, they run `analyze_reading_list` to get a clear breakdown of progress and an estimated completion date for the entire stack, allowing them to set realistic expectations. ### Studying for a certification exam A user has accumulated 50+ chapters of technical manuals. They use the MCP to get a comprehensive report that summarizes their overall completion status and calculates how many hours they must dedicate to studying based on their established reading speed. ## Benefits - Know exactly how much time is left. Instead of guessing, the `analyze_reading_list` tool calculates remaining hours based on total pages and your specific WPM. - Maintain focus by building momentum. The MCP uses the Snowball Method to automatically reorder your list, pushing you toward books that are nearly finished first. - Get a clear picture of progress. It generates a dashboard showing your overall completion percentage and the exact split between what's read versus unread material. - Stop losing track of metrics. The tool provides holistic progress analytics in a structured format, giving you reliable numbers for status updates. - Manage large backlogs efficiently. Use `analyze_reading_list` to process any array of reading items—tech books, papers, or manuals—in one go. ## How It Works The bottom line is: you get reliable metrics and actionable schedules without having to do the math yourself. 1. Pass your list of reading materials as a JSON array into the MCP. 2. The algorithmic engine processes the data, factoring in total pages and your specific WPM. 3. You receive a structured report that includes time estimates, completion percentages, and an optimized reading sequence. ## Frequently Asked Questions **How does analyze_reading_list calculate my remaining reading time?** It calculates the time using a strict algorithm based on total unread pages and the WPM you provide. It doesn't guess; it runs a precise calculation against your set rate. **Can analyze_reading_list reorder my documents?** Yes, it uses the Snowball Method to automatically sort your queue. This means it prioritizes books you are closest to finishing, followed by the shortest unread items for maximum momentum. **What kind of output does analyze_reading_list provide?** It generates a real-time JSON dashboard summarizing your progress. This includes total completion percentage, pages read vs. unread, and current pipeline status. **Does the MCP handle mixed formats (papers and books)?** Yes. The tool processes an array of reading items regardless of their source—be it a research paper or a tech book—as long as the required fields are present in your JSON input. **What specific data format must I use when calling analyze_reading_list?** You must provide a JSON string containing an array of reading items. Each item needs to include fields for page count, current progress percentage, and the title. The tool requires this structured input to run its calculations correctly. **How fast is analyze_reading_list when I have hundreds of books?** The process runs quickly because it uses a pure JavaScript runtime engine. This zero-dependency architecture ensures absolute speed, even with large backlogs of data. **What should I do if the input for analyze_reading_list is incomplete or wrong?** The tool handles bad inputs gracefully. If a required field is missing or the data violates constraints, it returns a clear error message. This lets you fix the input before generating any reports. **Are there any usage limits I need to be aware of when running analyze_reading_list?** Vinkius manages service stability across all MCPs. While we don't impose strict rate limits, continuous high-volume requests might trigger temporary throttling. Always check the Vinkius dashboard for current operational status. **How does it estimate the time remaining?** The algorithmic engine multiplies your remaining unread pages by an industry-standard 300 words-per-page. It then divides that massive word count by your specific reading speed (defaulting to 250 Words Per Minute) to output an exact hour count. **What is the Snowball Method sequence?** It is a psychological productivity framework. The algorithm sorts your 'reading' books by how close you are to finishing them. For completely unread books, it sorts them from shortest to longest. This guarantees you secure 'quick wins' fast to build reading momentum. **Can it process dozens of books at once?** Yes. Because it uses pure JSON and mathematical mapping without LLM token limits, it can instantly evaluate libraries containing thousands of entries without any calculation hallucination.