XuLei

Summary#

Initial Question#

Basic Information on MCP#

The definition and background of MCP: MCP, short for Model Context Protocol, is an open protocol that allows AI models to seamlessly integrate external data sources and tools. Through standardized interfaces, MCP addresses the connection problem between AI and data silos, enabling AI to access real-time information, perform operations, and provide responses based on accurate data. According to the introduction of Anthropic's Model Context Protocol, MCP consists of two components: the host and the server. The host, such as the Claude desktop client, acts as the AI interface, while the server connects to data sources and tools, such as Google Drive, Slack, and GitHub. This architecture is particularly suitable for scenarios requiring multi-source data integration.

The core value of MCP is to break down data silos, allowing AI to access and operate on external data and tools in real-time. This capability goes far beyond programming; it heralds a new paradigm of intelligence that can profoundly change the operational modes of various industries.

MCP may be the prototype of the next-generation intelligent interaction infrastructure. It represents a shift from a "model-centric" to a "data + model collaborative center," meaning AI is no longer an isolated black box but an agent rooted in real-world data.

Currently, MCP is used in programming scenarios, assisting programming in tools like the Claude client or AI IDE. Is this conversational interactive AI invocation method applicable in non-programming fields, especially in data-driven decision-making in the library industry?

The core of data-driven decision-making lies in:

1. Acquiring high-quality, multi-dimensional data: The quality of decisions heavily relies on the comprehensiveness and accuracy of data.
2. Real-time data insights: In a rapidly changing environment, decisions need to be based on the latest information.
3. Efficient data analysis and pattern recognition: Extracting valuable patterns and trends from vast amounts of data requires strong analytical capabilities.
4. Intelligent actions based on insights: Decisions ultimately need to translate into effective actions and be iteratively optimized based on feedback.

Traditional decision-making processes often face issues such as data silos, information lag, and low analytical efficiency. The emergence of MCP can precisely address these pain points, bringing revolutionary changes to data-driven decision-making.

AI-Driven Library Intelligent Interview#

Liu Beiling, Zhang Yuhui. The Application Prospects of Large Language Models in Library Acquisition and Editing [J]. Information and Management Research, 9(05):14-23.

1. From "Dynamic Interview Rules" to "Intelligent Execution of Rules"#

• Surface Understanding: The text emphasizes the importance and dynamism of interview rules, pointing out that LLMs can understand and execute these complex, non-quantifiable rules to achieve intelligent pre-selection of book lists.
• Deep Insight: This is not just simple rule automation but rather intelligent execution of rules. Traditional rule execution is mechanical and based on preset instructions. The involvement of LLMs means that rule execution can be more contextual, flexible, and humanized. For example, for the rule "For research monographs that have a significant impact on a certain discipline, two copies should be ordered," LLMs can not only recognize "research monographs" but also judge "significant impact" by analyzing literature influence, discipline development trends, etc., which transcends the limitations of traditional computer instructions.
• Innovative Thinking: This indicates a shift in the rule-making and execution model in libraries. Rules are no longer rigid texts but guiding principles that can be dynamically understood and flexibly applied by LLMs. In the future, rule-making may focus more on principles rather than details, leaving the execution of details to smarter AI systems. This requires rethinking the essence of rules: Are rules meant to better serve the library's goals or to restrict the library's actions? Can intelligent execution better balance the constraints and flexibility of rules?

2. From "Data Mining and Analysis" to "Proactive Prediction and Response to User Needs"#

• Surface Understanding: The text describes using LLMs to mine reader behavior data (search records, borrowing data) to generate reader demand book lists and analyze collection resources and predict popular book lists.
• Deep Insight: This reflects a shift in library acquisition work from passively responding to needs to actively predicting and guiding needs. Traditional acquisition work often relies on librarians' experience, publisher recommendations, and a small number of reader requests, which is relatively lagging and passive. In contrast, intelligent acquisition based on data mining can anticipate readers' potential needs and even predict future trends. This is not just about optimizing procurement but also about reshaping the relationship between libraries and readers, making libraries more like intelligent information service platforms rather than simple book storage places.
• Innovative Thinking: This data-driven model may give rise to new library service models. For example, libraries can plan reading activities and themed lectures in advance based on predicted popular book lists, actively guiding readers in reading and learning. This transcends the traditional "collection - borrowing" model, shifting towards a more proactive "information service - knowledge dissemination" model. At the same time, it is essential to be wary of data bias and algorithmic prejudice, avoiding excessive catering to "popular" while neglecting niche but important knowledge areas. How to balance "popular demand" and "academic depth"? How to avoid a data-driven "information cocoon"?

From "AI Interactive Interview Book List Configuration" to "Human-Machine Collaborative Acquisition Workflow"#

This scenario focuses on constructing a human-machine collaborative interview work model. By developing an acquisition dialogue robot, librarians can interact with the AI system using natural language to complete book list configurations and interview assistance without complex commands. Librarians can input needs through dialogue, such as specifying book list types, themes, budgets, etc., while the AI system uses retrieval-enhanced generation technology to search the interview rules database and external bibliographic databases, intelligently generating corresponding book lists and procurement suggestions based on the librarian's dialogue intent.

This interactive approach makes the interview work more flexible and efficient. Librarians can use the dialogue robot as an intelligent assistant to quickly obtain the required book list information and analysis results, aiding daily acquisition decisions. For example, librarians can request the generation of bestseller lists, children's literature procurement suggestions, literary award book databases, or analyze supplier-provided book lists to obtain risk alerts, compliance checks, and information completion services. The core lies in achieving collaborative work between humans and AI through natural language dialogue, enhancing the intelligence level and work efficiency of interviews.

The most important value of MCP intelligent interviews that I can think of is real-time data analysis through natural language dialogue interaction, assisting in specifying interview decisions. Based on the above information, please combine the design philosophy of MCP to conduct a more in-depth discussion and analysis of AI-driven library intelligent interviews.

Initial Thought Process#

Well, the user's question is about the potential application of MCP (Model Context Protocol) in data-driven decision-making in the library industry. First, I need to thoroughly understand the core mechanism of MCP. According to the user's description, MCP allows AI models to integrate external data sources and tools, connecting various data sources like Google Drive, Slack, GitHub, etc., through a host and server architecture. In the programming field, the Claude client has already applied MCP to enhance development efficiency. Now the user wants to know if this interactive method is applicable in the non-programming field of libraries, especially in data-driven decision-making.

Next, I will review the four core points of data-driven decision-making mentioned by the user: high-quality data, real-time insights, efficient analysis, and intelligent actions. Then, the issues with traditional decision-making processes—data silos, information lag, and low efficiency. The advantages of MCP precisely address these problems, but what challenges might arise in its specific application in the library industry?

First, it is essential to understand the types of decisions in the library industry. This may involve procurement decisions, service optimization, space management, user behavior analysis, etc. For example, procurement decisions need to combine borrowing data, user feedback, budget information, etc. Service optimization may require real-time monitoring of foot traffic and user satisfaction data. If this data is scattered across different systems or departments, forming data silos, then MCP's integration capability can play a role.

However, specific application scenarios need to be analyzed in detail. For instance, in the library industry mentioned by the user, data sources may include library management systems, electronic resource platforms, user survey systems, social media feedback, IoT devices (such as smart bookshelf sensors), etc. Can MCP seamlessly connect these different data sources? It may require customized server components to interface with private data sources, which poses certain requirements for the library's technical architecture.

Then, how does the "dialogue interactive intelligent agent invocation method" mentioned by the user adapt to non-programming environments? Library managers may not have programming skills, so a natural language interactive interface is needed, allowing users to pose questions or needs through dialogue, such as "Analyze the popular borrowing trends of the past three months." At this point, MCP needs to understand the request, call relevant data sources, analyze, and generate reports or suggestions. This is technically feasible, but the key lies in whether the model's natural language processing capability is accurate enough and the standardization of data interfaces.

Another consideration is real-time capability. Some library decisions may require immediate data, such as current visitor numbers and the status of books on shelves. If MCP can connect in real-time to IoT devices or real-time databases, it can provide timely analysis. For example, adjusting library opening hours or layout requires real-time foot traffic data.

Moreover, privacy and security issues are also crucial. Libraries handle data that may include user privacy, such as borrowing records, so when connecting data sources through MCP, it is essential to ensure the security of data transmission and access, complying with relevant regulations like GDPR.

Additionally, library data may consist of both structured and unstructured data, such as borrowing records being structured while user feedback may be in textual form as unstructured data. MCP needs to have the capability to handle multi-modal data to conduct effective analysis after integration. For example, combining borrowing data and user textual feedback to analyze the popularity of a certain type of book and its reasons.

Furthermore, library decisions may require cross-departmental collaboration, such as data integration between the procurement department and the technical services department. MCP can help break down data barriers between these departments, providing a unified decision support platform. For example, procurement decisions should not only be based on historical borrowing data but also consider budget, supplier information, inventory status, etc., which may be stored in different systems.

However, implementing such a system may encounter challenges in technical integration. For instance, existing systems may use different APIs or data formats, requiring the development of adapters to connect to the MCP server. Additionally, libraries may need to train staff to use the new interactive interface and change existing workflows, which involves change management issues.

Moreover, cost factors also need to be considered. Deploying MCP may require a certain investment in IT infrastructure, which may be difficult for resource-limited small libraries to afford. However, if MCP is offered as a cloud service, it may reduce upfront costs.

In summary, the applicability of MCP in data-driven decision-making in the library industry depends on several key factors: the ability to connect data sources, the ease of natural language interaction, real-time data processing capability, multi-modal data integration, security, and implementation costs. In feasible scenarios, such as optimizing resource procurement, enhancing service personalization, and dynamically managing spaces, MCP has the potential to significantly improve decision efficiency and quality, but specific adaptation plans and possible technical support are needed.

Based on the above information, please answer my question: The most important value of MCP intelligent interviews is real-time data analysis through natural language dialogue interaction, assisting in specifying interview decisions. Please combine the design philosophy of MCP to conduct a more in-depth discussion and analysis of AI-driven library intelligent interviews.

Initial Response#

In-Depth Discussion and Analysis of AI-Driven Library Intelligent Interviews: Based on MCP Philosophy#

Your core point is very accurate: The most important value of MCP-driven intelligent interviews lies in conducting real-time data analysis through natural language dialogue interaction, thereby assisting libraries in making wiser acquisition decisions. This not only summarizes the current intelligent interview model but also profoundly reveals the potentially revolutionary impact of the MCP philosophy in the library field.

To delve deeper into the discussion and analysis, we can expand on the following key dimensions, further illustrating its value and significance in library intelligent interviews in conjunction with the design philosophy of MCP:

1. Natural Fit of MCP Philosophy and Library Intelligent Interviews: Breaking Down Information Barriers and Achieving Data Fusion

The core philosophy of MCP is to break down data silos and connect AI with real-world data and tools. This aligns perfectly with the long-standing issues of data fragmentation and information barriers faced by the library industry.

• Data Fragmentation in Libraries: Library data is often scattered across different systems, such as:
  • Library Management Systems (LMS): Recording collection information, borrowing data, reader information, etc.
  • Electronic Resource Platforms: Providing usage data for e-books, journals, databases.
  • Interlibrary Loan Systems: Recording interlibrary borrowing data.
  • Procurement Systems: Recording order information, supplier information, budget information.
  • User Feedback Systems: Collecting reader opinions, recommendation information, etc.
  • Academic Literature Databases/Bibliographic Databases: Such as Douban Reading, OpenAlex, WorldCat, etc., providing broader book information, evaluations, academic influence, etc.
• Pain Points of Traditional Acquisition Decisions: When making acquisition decisions, librarians need to manually collect data from these fragmented systems, organize and analyze it, which is inefficient and prone to information bias. Data silos hinder libraries' comprehensive understanding of reader needs and optimal allocation of collection resources.
• MCP's Solution: The MCP architecture allows the intelligent interview system (as the host) to seamlessly connect to the various data sources (as servers) mentioned above. Through standardized interfaces, AI models can access and integrate data from different systems in real-time, forming a unified data view. This fundamentally breaks down data silos, providing a comprehensive and multi-dimensional data foundation for intelligent interviews.

2. Natural Language Dialogue Interaction: Lowering the Usage Threshold and Enhancing Human-Machine Collaborative Efficiency

MCP emphasizes natural language dialogue interaction, which is crucial for the library industry as it significantly lowers the technical threshold, enabling librarians without technical backgrounds to easily leverage AI's power.

• Limitations of Traditional Interview Tools: Traditional interview tools may require librarians to learn complex system operations, data query languages (like SQL), etc., limiting their usability and accessibility.
• Advantages of Dialogue Interaction: Based on MCP, the intelligent interview system allows librarians to communicate with AI in natural language, just like conversing with an experienced assistant. For example:
  • "Please help me generate a list of the top 10 most borrowed books in the humanities and social sciences over the past year."
  • "Analyze the recent reader evaluations of newly arrived literary books."
  • "Compare the book lists provided by Publisher A and Publisher B to see which aligns better with our library's procurement strategy."
• Collaborative Workflow: This conversational interaction allows librarians to focus on the core issues of acquisition decisions while delegating tedious data collection and analysis tasks to AI. Librarians can pose questions, express needs, and provide feedback, while AI handles data retrieval, analysis, report generation, and suggestions. This truly achieves a human-machine collaborative acquisition workflow, enhancing work efficiency and decision quality.

3. Real-Time Data Analysis: Grasping Rapidly Changing Needs and Enhancing Decision Agility

The real-time data access capability of MCP enables intelligent interviews to make decisions based on the latest information, which is crucial for quickly responding to reader needs and market changes.

• Lagging Nature of Traditional Acquisition Decisions: Traditional acquisition decisions often rely on past data and experience, making it difficult to quickly capture changes in reader interests and new publishing trends.
• Value of Real-Time Data Analysis: The intelligent interview system based on MCP can:
  • Monitor borrowing data in real-time: Quickly identify popular and unpopular books, understand the utilization of collection resources.
  • Analyze reader search behaviors in real-time: Gain insights into current research interests and information needs of readers.
  • Track discussions on social media and academic communities in real-time: Understand the latest academic hotspots and book evaluations.
  • Obtain real-time publishing information and market trends: Stay updated on the latest publishing dynamics and book sales data.
• Agile Acquisition Decisions: Through real-time data analysis, libraries can more agilely adjust acquisition strategies, such as:
  • Quickly procuring popular books to meet immediate reader needs.
  • Timely adjusting collection structures to optimize resource allocation.
  • Modifying acquisition rules and strategies based on real-time feedback.

4. Beyond Assisting Decisions: Moving Towards Proactive Services and Personalized Collection Development

The value of MCP-driven intelligent interviews extends beyond assisting decisions; it indicates that libraries can shift from passively responding to needs to proactively serving and achieving more personalized collection development.

• From Passive Response to Proactive Prediction: Based on data mining and predictive analysis, the intelligent interview system can:
  • Predict potential reader needs and procure relevant books in advance.
  • Forecast popular book lists and plan reading activities and resource promotions ahead of time.
  • Push personalized book recommendations and resource services based on user profiles.
• Personalized Collection Development: By analyzing reader behavior data and personalized needs, the intelligent interview system can help libraries build collections that better meet reader demands, enhancing the utilization of collection resources and reader satisfaction.
• Strategic Significance: This shift transforms libraries from mere storage places for books into intelligent information service platforms that can proactively provide precise, personalized knowledge services, enhancing the social value and influence of libraries.

5. Potential Challenges and Future Outlook

Despite the enormous potential of MCP-driven intelligent interviews, practical applications may face several challenges:

• Complexity of Data Integration: Different library systems may have varying data formats and interface standards, requiring adaptation and integration.
• Ensuring Data Quality: The accuracy of data analysis relies on data quality, necessitating the assurance of data accuracy, completeness, and timeliness.
• Privacy and Security Issues: Strict adherence to data privacy protection regulations is required to ensure user data security.
• Enhancing Librarian Skills: Librarians need to adapt to new work modes, learn how to collaborate with intelligent systems, and improve data literacy and information analysis capabilities.
• Technical Costs and Maintenance: Deploying and maintaining MCP systems may require a certain level of technical investment and costs.

Future Outlook:

As MCP technology continues to mature and proliferate, along with the rapid development of AI technology, we have reason to believe that MCP-driven intelligent interviews will play an increasingly important role in the library field. Future library acquisition work will become more intelligent, efficient, and personalized, allowing libraries to better serve the mission of social knowledge dissemination and cultural inheritance.

Summary:

The application of the MCP philosophy in library intelligent interviews has its core value in real-time integration and analysis of multi-source data through natural language dialogue interaction, assisting librarians in making smarter and more agile acquisition decisions. This not only enhances acquisition efficiency and decision quality but also signifies that libraries will transition from passive responses to proactive services, building a more personalized and intelligent information service platform, ultimately achieving a profound transformation in library service models. This is a thorough elaboration and strong argument for the core point you initially raised.

Scenario Simulation#

Question#

Please simulate a day in the daily work of a library acquisition librarian, describing the business scenario using MCP-like AI intelligent interview tools.

Answer#

Alright, let's simulate a day in the daily work of a library acquisition librarian, Ms. Li, focusing on how she uses MCP-like AI intelligent interview tools and showcasing specific business scenarios.

Character: Ms. Li, a librarian in the acquisition department of a university library, responsible for acquiring humanities and social sciences books, experienced and open to new technologies.

Tool: LibSmart-Acq, an AI intelligent acquisition assistant based on the MCP architecture, capable of natural language dialogue interaction, connecting multiple internal and external data sources, providing real-time data analysis and acquisition decision support.

A Day in Ms. Li's Life:

8:30 AM - 9:00 AM Morning Preparation and System Check

Ms. Li enters her office, first opening her computer and launching the LibSmart-Acq system.

[Scene 1: System Startup and Personalized Daily Report]

The interface of LibSmart-Acq is user-friendly. Ms. Li wakes the system with a natural language command: "Good morning, LibSmart!"

The system responds: "Good morning, Ms. Li! Your acquisition assistant LibSmart is at your service. I have prepared today's acquisition work report for you."

The screen displays a personalized daily report, including:

• Collection Dynamics Briefing: Top 10 most borrowed books yesterday (humanities and social sciences), list of books with collection copy warnings (high borrowing rate, insufficient copies).
• New Book Notifications: Today's new book announcements from major publishers (in the humanities and social sciences), a list of recommended new books (based on academic hotspots and expert recommendations).
• Pending Tasks Reminder: Reader recommendation book lists awaiting review, supplier A's book list awaiting analysis, last week's acquisition work report awaiting submission.
• System Health Status: Data source connection status (library management system, e-book platform, Douban Reading API, etc.), system running status is good.

Ms. Li quickly browses the report and gains a preliminary understanding of today's key tasks. She notices a few recently popular social science books in the "collection copy warning list" and decides to prioritize the copy increase.

9:00 AM - 10:30 AM Collection Data Analysis and Copy Increase Decision

[Scene 2: Natural Language Query and Real-Time Data Analysis]

Ms. Li says to LibSmart-Acq: "LibSmart, check the reference books for the course 'Classical Theories in Sociology' and analyze the collection copy situation and borrowing demand."

The system quickly responds, retrieving data from the library management system and connecting to the course information database. Moments later, the screen displays:

• Course Reference Book List: Lists textbooks and reference books such as "The Structure of Sociological Theory," "Research Methods in Sociology," "Modern Sociological Theory," etc.
• Collection Copy Analysis: Detailed data on the number of copies in the collection, current copies in the library, total borrowing times, average borrowing period, number of reservations, etc.
• Demand Forecast: Based on historical borrowing data and course enrollment numbers, it predicts the borrowing demand for this book list over the next month.

Ms. Li carefully reviews the data and finds that "The Structure of Sociological Theory" has a very high borrowing rate and many reservations, but there are only 3 copies in the collection, which is clearly insufficient. She decides to increase the copies.

[Scene 3: Conversational Copy Increase Decision]

Ms. Li continues the dialogue with LibSmart-Acq: "LibSmart, how many copies of 'The Structure of Sociological Theory' should we increase? Please refer to the acquisition rules database and budget situation."

The system retrieves the preset acquisition rules database (e.g., core course reference books, high borrowing rates, many reservations can be appropriately increased; budget limits, etc.) and combines it with the current acquisition budget balance to provide a suggestion:

"Based on your acquisition rules and current data analysis, it is recommended to increase 5-8 copies of 'The Structure of Sociological Theory.' The current budget balance is sufficient; it is suggested to increase 8 copies to meet future borrowing demand."

Ms. Li agrees with the system's suggestion and confirms the increase of 8 copies. LibSmart-Acq automatically generates a draft procurement order and sends it to the colleague responsible for procurement for review.

10:30 AM - 12:00 PM Reader Recommendation List Review and Intelligent Screening

[Scene 4: Intelligent Review and Batch Processing]

Ms. Li switches to the "Reader Recommendation List" interface, where LibSmart-Acq has already conducted preliminary intelligent review and classification of the list:

• "High Priority Recommendations": The system identifies recommendations that align with collection development directions, have high academic value, and are in urgent demand from readers, such as academic monographs and classic works.
• "Pending Manual Review": Recommendations that the system cannot automatically judge and require further manual review, such as popular literature, duplicate recommendations, incomplete information, etc.
• "Not Recommended for Procurement": Recommendations deemed not to align with collection policies, already have multiple copies in the collection, or are clearly unreasonable.

Ms. Li first processes the "High Priority Recommendations" list. LibSmart-Acq provides for each recommendation:

• Basic Book Information: Title, author, publisher, publication date, Douban rating, reader evaluations, etc.
• Collection Duplication Check: Automatically searches the collection and prompts whether it is already in the collection or in the process of procurement.
• Academic Influence Analysis: Connects to academic databases to provide citation counts and expert evaluations for the book (for academic works).
• Procurement Channels and Price Suggestions: Based on book information and historical procurement data, recommends suitable suppliers and reference prices.

Ms. Li quickly reviews the "High Priority Recommendations" list and clicks "Agree to Procure" for most recommendations, automatically adding these recommendations to the procurement list.

For the "Pending Manual Review" list, Ms. Li carefully examines each recommendation and interacts with LibSmart-Acq to assist in her judgment.

[Scene 5: Conversational Recommendation Review and Information Completion]

Reader Recommendation Information: Title: "The Future of AI," Author: Unknown, Publisher: Unknown.

Ms. Li asks LibSmart-Acq: "LibSmart, help me check 'The Future of AI' and see if there are matching book information."

The system connects to bibliographic databases like Douban Reading and Dangdang, quickly retrieving and providing multiple matching results:

• Result 1: "AI: The Future," Author: Li Kaifu, Publisher: XX Publishing House, Publication Date: 2017.
• Result 2: "Artificial Intelligence: The Future," Author: Nick Bostrom, Publisher: YY Publishing House, Publication Date: 2016.
• Result 3: …

Ms. Li reviews the matching results and, based on the book cover, author information, and publication date, determines that the reader likely wants to recommend "AI: The Future" by Li Kaifu. She selects Result 1, and LibSmart-Acq automatically completes the recommendation information and adds it to the "High Priority Recommendations" list, awaiting procurement.

12:00 PM - 1:30 PM Lunch Break

1:30 PM - 3:00 PM Supplier Book List Analysis and Risk Assessment

[Scene 6: Intelligent Analysis of Supplier Book List and Risk Alerts]

In the afternoon, Ms. Li needs to process a new book list sent by Supplier A. She imports the book list file into LibSmart-Acq.

The system automatically conducts intelligent analysis of the book list:

• Duplicate Book Detection: Automatically identifies duplicate books in the list with the collection and marks them.
• Price Anomaly Detection: Compares historical procurement prices and market prices to identify books with significantly high prices and issues alerts.
• Publisher Reputation Assessment: Analyzes the academic reputation, publishing quality, and supply stability of the publisher, providing a risk assessment.
• Subject Relevance Analysis: Analyzes the matching degree of the book list with the library's collection development direction based on book information and subject classification systems.

LibSmart-Acq generates a detailed analysis report, pointing out issues in Supplier A's book list:

• High Duplicate Books: 15% of the books in the list are duplicates of the collection.
• Prices Slightly Above Market Average: Some book prices are 5%-10% higher than average.
• Low Reputation Rating for Publisher B: The book list includes books from XX Publishing House, which has recently had poor supply stability; cautious procurement is advised.

Ms. Li carefully reads the report and interacts with LibSmart-Acq to understand the risk details further.

[Scene 7: Conversational Risk Assessment and Procurement Strategy Adjustment]

Ms. Li asks: "LibSmart, please explain the risk assessment report for XX Publishing House in detail and provide procurement suggestions."

The system retrieves historical data and industry evaluation information for XX Publishing House, providing a more detailed report:

"XX Publishing House has recently experienced internal adjustments, leading to extended publishing and supply cycles and increased return rates. Recommendations: For books from XX Publishing House, consider reducing procurement quantities or choosing other suppliers with stable supply. It is advisable to prioritize similar books from YY Publishing House and ZZ Publishing House, as these publishers have a good reputation and stable supply in the humanities and social sciences."

Based on LibSmart-Acq's risk alerts and suggestions, Ms. Li adjusts the procurement strategy for Supplier A's book list, reducing the procurement quantity for books from XX Publishing House and increasing the procurement of similar books from YY Publishing House and ZZ Publishing House.

3:00 PM - 4:00 PM Popular Book List Prediction and Themed Book Exhibition Planning

[Scene 8: Popular Book List Prediction and Thematic Planning Assistance]

Ms. Li wants to plan a themed book exhibition for next month's "World Book Day." She hopes LibSmart-Acq can provide some popular book lists and theme suggestions.

Ms. Li says to LibSmart-Acq: "LibSmart, predict the popular borrowing book list in the humanities and social sciences for next month and recommend several suitable themes for the 'World Book Day' exhibition."

The system analyzes recent borrowing data, reader search keywords, social media reading topics, etc., predicting the popular book list for next month and recommending several exhibition themes based on the "World Book Day" holiday theme:

• Popular Book List Prediction: Lists the 20 books predicted to have the highest borrowing volume in the humanities and social sciences next month.
• Exhibition Theme Suggestions:
  • "The Power of Reading: Exhibition of Classic Humanities and Social Sciences Works"
  • "Exploring the Unknown: Exhibition of Cutting-Edge Social Science Research Achievements"
  • "Reading for Life: Heartwarming Stories in Social Sciences"
  • "Dialogue with the World: Exhibition of Social Science Books from a Multicultural Perspective"

Ms. Li references the popular book lists and theme suggestions provided by LibSmart-Acq, combining them with the library's actual situation and collection characteristics, ultimately determining the exhibition theme as "The Power of Reading: Exhibition of Classic Humanities and Social Sciences Works," and begins preparing for book selection and publicity for the exhibition.

4:00 PM - 5:00 PM Work Summary and Tomorrow's Plan

[Scene 9: Work Summary and Intelligent Assistant]

As the day nears its end, Ms. Li asks LibSmart-Acq to summarize today's work.

Ms. Li says: "LibSmart, summarize today's work and generate a work report."

The system automatically reviews Ms. Li's work records for the day and generates a brief work report:

• Today's Work Summary:
  • Processed 2 types of collection copy warning books and completed copy increase decisions.
  • Reviewed XX reader recommendation lists, agreed to procure XX, and rejected XX.
  • Analyzed Supplier A's book list, identified risks, and adjusted procurement strategies.
  • Predicted next month's popular book list and assisted in planning the "World Book Day" exhibition.
• Pending Tasks Reminder: Supplier A's procurement order draft awaiting review, last week's acquisition work report awaiting submission.

Ms. Li reviews the work report, confirms it is correct, and submits the report. She tells LibSmart-Acq: "Thank you, LibSmart, today's work is done, see you tomorrow!"

The system responds: "You're welcome, Ms. Li! Looking forward to serving you again tomorrow! Have a great day!"

Ms. Li happily closes the LibSmart-Acq system, tidies up, and leaves for the day. She feels that with the help of the AI intelligent interview assistant, her work efficiency has greatly improved, allowing her more time and energy to focus on more creative and strategic tasks, such as collection planning and reader service innovation. She is full of anticipation for the intelligent future of libraries.

Summary:

Through the simulation of Ms. Li's daily work, we can see that the AI intelligent interview tool LibSmart-Acq, based on the MCP architecture, conducts real-time data analysis and intelligent decision support through natural language dialogue interaction, significantly enhancing the work efficiency and decision-making level of library acquisition librarians. It plays a crucial role in collection data analysis, copy increase, reader recommendation review, supplier book list analysis, popular book list prediction, and thematic event planning, truly achieving a human-machine collaborative intelligent acquisition workflow.