Database installation and deployment method based on MCP protocol

Abstract

The invention discloses a database installation and deployment method based on an MCP protocol, and relates to the technical field of database deployment. In order to solve the problems of high requirement on professional knowledge of a user and tedious privacy configuration in traditional database automatic deployment, the scheme adopted by the invention comprises the following steps: realizing standardized configuration of privacy information based on an MCP protocol, and completing related settings by the user through an operation interface; the method comprises the following steps of: receiving a natural language deployment instruction input by a user and triggering a deployment process by virtue of natural language analysis capability of an MCP protocol: calling MCP Tools by an MCP Server to acquire full-amount environment information of a server, and matching to obtain an adaptive installation package version; detecting dependence conditions of a target environment, automatically supplementing missing software packages, and automatically executing database installation, initialization configuration and monitoring component deployment; and the MCP Server summarizes the state information of the whole process and returns a deployment result. According to the invention, automatic and rapid installation and deployment of the database can be realized.

Description

Technical Field

[0001] The present invention relates to the technical field of database deployment, and in particular to a database installation and deployment method based on the MCP protocol. Background Art

[0002] 1. Current status of development in the technology field As enterprises deepen their digital transformation, databases, as core infrastructure for data storage and processing, face key technical challenges in installation and deployment, including efficiency, reliability, and compatibility. Traditional database deployment processes rely on manual operations, requiring a series of steps, including environmental testing, installation package matching, dependent software installation, parameter configuration, and cluster initialization. These steps involve adapting to multiple dimensions, including server hardware architecture (x86 / ARM), operating system (Linux / Windows / domestic operating systems), and network environment. These operations are complex and prone to errors. For distributed database clusters, manual configuration of node roles, data synchronization policies, and failover mechanisms is also required. This is time-consuming, labor-intensive, and difficult to standardize, and has become a bottleneck in enterprise IT infrastructure development.

[0003] 2. Defects and Deficiencies of Existing Technologies 1. Manual intervention predominates, resulting in low deployment efficiency and a high barrier to entry. Traditionally, operations personnel must manually perform environment checks through the command line (e.g., using lscpu to check the CPU architecture and dpkg -l to check for dependencies) and select installation package versions based on experience. If dependent components (such as OpenSSL and the Python runtime) are missing from the environment, they must manually search for matching versions and compile and install them. With this approach, single-node deployment takes an average of 4-6 hours, while cluster deployment can take as long as 1-2 days. Furthermore, non-expert users, lacking hardware and system knowledge, struggle to complete deployment independently. Furthermore, the manual error rate exceeds 30%.

[0004] 2. Inadequate environmental adaptability and poor cross-platform compatibility. Due to environmental differences between hardware architectures (e.g., x86 servers vs. ARM servers) and operating system versions (e.g., CentOS 7 vs. Ubuntu 22.04), installation packages require targeted customization. However, traditional solutions lack automated environment awareness and version matching mechanisms. Deployment often fails due to dependency library version conflicts (e.g., incompatible GCC versions) or hardware architecture mismatches. The cross-platform deployment success rate is only 60%-70%, making it difficult to meet the rapid adaptation requirements of multi-cloud environments and domestic hardware (e.g., Zhaoxin and Kunpeng).

[0005] 3. Privacy configuration is fragmented, posing significant security risks. Database deployment involves sensitive information such as administrator passwords, encryption certificates, and network access whitelists. Traditional solutions often hard-code these privacy parameters in plain text within scripts or configure them in a decentralized manner through non-standard interfaces. These solutions also lack a unified security verification and auditing mechanism, which can easily lead to sensitive information leaks or uncontrolled configuration risks.

[0006] 4. Cluster deployment is highly complex, and ensuring context consistency is difficult. In distributed database cluster scenarios, each node's configuration information (such as IP addresses, port numbers, and storage paths) relies on manual maintenance, which can easily lead to context inconsistencies (for example, a node missing a firewall rule, leading to cluster communication interruption). Traditional solutions lack the ability to centrally manage environmental information such as hardware resources, network topology, and data models. Cluster initialization and troubleshooting require manual node-by-node verification, which is not only inefficient but also reduces system reliability due to human error.

[0007] 3. Limitations of existing automation solutions Existing technologies improve deployment efficiency through script automation (Shell / Python), containerization (Docker), or orchestration tools (Kubernetes), but there are significant shortcomings in actual applications.

[0008] Scripted solution: Relying on pre-written fixed scripts, when the deployment environment changes (such as configuration parameter adjustments, dependency version updates), the script code needs to be frequently modified. This not only has high maintenance costs, but also is difficult to adapt to complex and changing scenarios and has extremely poor flexibility.

[0009] Containerization: Although images can be used to isolate applications from the environment, manual configuration of dependencies (such as library files and system tools) is still required during image building. Furthermore, the image is highly dependent on the host machine's kernel version and hardware resources (such as CPU architecture and memory capacity), making deployment failures prone to environmental differences.

[0010] Orchestration tools: These tools focus on resource scheduling and service management during cluster runtime. They offer weak support for key pre-deployment steps (such as environmental compatibility testing, installation package version matching, and permission pre-verification). These tools often require secondary development in conjunction with other tools, failing to form a complete automated closed loop of "environmental perception, decision-making, and execution," leading to fragmented deployment processes. Summary of the Invention

[0011] The present invention provides a database installation and deployment method based on the MCP protocol to address the problems of traditional database automation deployment, such as high requirements for user expertise, inefficient machine information perception, frequent manual intervention, and cumbersome privacy configuration.

[0012] The present invention provides a database installation and deployment method based on the MCP protocol, which solves the above technical problems using the following technical solutions: A database installation and deployment method based on the MCP protocol includes the following steps: S1. Implement standardized configuration of privacy information based on the MCP protocol, allowing users to complete relevant settings of privacy information through the operation interface; S2. Leveraging the MCP protocol's natural language parsing capabilities, the MCP Server receives natural language deployment instructions entered by the user and triggers the deployment process. The MCP Server, acting as the core processing hub, calls MCP Tools to collect the server's full environment information, matches the appropriate installation package version, and, based on the server's network status, determines whether the installation package needs to be transmitted online. The MCP Server then schedules the installation package. S3. Detect the target environment's dependency conditions based on the selected installation package and automatically install any missing software packages; then automatically perform database installation, initial configuration, and monitoring component deployment. S4. The MCP Server summarizes the status information of the entire process and returns the deployment result.

[0013] Optionally, the MCP Tools involved are lightweight cross-platform tool components that automatically collect full environment information of the server; The MCP Server serves as the decision-making hub. It uses the MCP protocol to standardize the privacy information configuration format, parses user instructions through a natural language interactive interface, and automatically triggers the database installation and deployment process. At the same time, a unified environment model is defined through the MCP protocol, and the context parameters of hardware, software, and security policies are standardized, enabling the MCP Server to dynamically adjust deployment strategies based on real-time environmental information and achieve non-discriminatory adaptation across cross-platform environments.

[0014] Optionally, in step S1, the user enters the privacy information of the database deployment through a preset page or API in a standardized format configured by the MCP protocol, including server login credentials, administrator account password, data encryption certificate and key, network access whitelist, and data storage path and permissions; The privacy information entered by users through preset pages or APIs will serve as the core basis for security authentication, data encryption, and access control in the subsequent deployment process, ensuring that the deployment process complies with privacy protection and permission management requirements, while providing MCPServer with basic parameters for executing security policies.

[0015] Further optionally, when executing step S2, based on a preset page or API, the user inputs deployment requirements in natural language; The MCP Server calls the parsing rules in the prompt vocabulary and uses the natural language engine to break down the key elements: database type, version, deployment form, and target environment; Subsequently, the MCP Server retrieves the deployment policy template for the corresponding environment from the knowledge base, combines it with the security parameters in the privacy information configuration, and generates a standardized deployment work order containing execution steps, resource requirements, and security constraints, converting the user's natural language requirements into automated tasks.

[0016] Preferably, the prompt word library stores a set of natural language instructions and deployment task mapping rules, which is used by the natural language parsing engine of the MCP Server to identify user intentions and generate corresponding deployment work orders; The knowledge base involved stores the environment model, dependencies, and deployment strategies required for database deployment, which is an important basis for MCPServer decision-making.

[0017] Optionally, step S2 is performed, where the MCP Server, as the core processing hub, calls MCP Tools to collect the full environment information of the server and matches the adapted installation package version, specifically including: After converting the user's natural language requirements into automated tasks, the MCP Server distributes the lightweight tool MCP Tools to the target server, triggering the collection of full environmental information. Hardware information includes CPU architecture, memory capacity, storage type and capacity; software information includes operating system version, kernel parameters, and installed software; and network information includes IP address, available ports, firewall rules, and network bandwidth. After the collection is completed, the MCP Server compares the information with the environment model in the knowledge base and automatically selects the appropriate installation package and deployment parameters to ensure that subsequent deployment is compatible with the hardware and software characteristics of the target environment.

[0018] Further optionally, step S3 specifically includes: S3.1. Based on the selected installation package, the MCP Server first checks whether the local cache exists. If so, it directly calls the package. If online access is required, it is downloaded from the specified source as needed based on the network bandwidth parameters and installation package size in the environment model. The database configuration file is automatically generated based on the environment information and the configuration template in the knowledge base. S3.2. For distributed clusters, automatically start each node service, assign replica roles based on node hardware performance, complete cluster handshake and data synchronization through internal communication protocols, activate database services, and create initial accounts and permissions based on administrator information in the privacy configuration, completing the deployment and activation of core database functions. S3.3. After the database is deployed, real-time monitoring of the database's operating status and operation and maintenance support are achieved through probe distribution, indicator collection, alarm strategy configuration, and visual display.

[0019] Further optionally, the step S3.3 specifically includes: S3.3.1. The MCP Server automatically distributes monitoring probes to target servers. The probes collect key operational indicators in real time: resource utilization, database performance indicators, and cluster status. S3.3.2. Configure the probe's alarm threshold and notification method based on the alarm strategy of the environment model in the knowledge base; S3.3.3. Connect the probe data to the visual monitoring platform to generate a real-time dashboard to intuitively display the database operation status and provide data support for subsequent operation and maintenance.

[0020] Further optionally, step S4 specifically includes: S4.1. The MCP Server uses log collection tools to aggregate the execution data from the preceding steps, including timelines, environment information summaries, configuration parameter details, manual interaction records, and execution results. S4.2. Generate a structured deployment report based on the summarized data and provide feedback through the user's specified channels. The deployment report contains the final status of the deployment results and detailed logs of each link, marking the closed loop of the entire process from "user request" to "deployment completion" and providing data basis for subsequent optimization of deployment strategies.

[0021] Optionally, an intelligent decision-making assistance AI module is pre-set, which integrates the historical fault handling knowledge base, process node execution rules and environmental parameter analysis model to provide automated decision support during the intervention process; During steps S1-S4, if a critical point or unexpected stuck point is encountered, three levels of manual intervention are supported. The specific intervention process is as follows: (1) During the execution of each node in the installation and deployment process, the node operation status is monitored in real time. When an execution failure or timeout is detected in the current node, a first-level intervention is immediately triggered; if no abnormality is detected, the process automatically proceeds to the next node; (2) For transient problems such as temporary network fluctuations or file lock conflicts that can be recovered through retries, the first-level intervention automatically calls the AI ​​retry mechanism driven by the intelligent decision-making assistance AI module to re-execute the current node operation according to the preset rules: (2a) If the retry is successful, the AI ​​retry log is recorded, including the failed node ID, the time of the first failure, the number of retries, and the recovery time, and the process seamlessly connects to the next node; (2b) If the attempt fails after the number of retries specified in the pre-set rules, a secondary intervention is triggered; (3) When the AI ​​retry mechanism fails and a matching solution exists in the knowledge base, the secondary intervention invokes the user intervention retry mechanism. The intelligent decision-making assistance AI module generates options containing "stuck point type + environmental information + up to 3 recommended actions" and pushes the option information to the user. The user selects an action and manually initiates a retry: (3a) If the retry is successful, the user intervention log is recorded, including the user operation content and retry result, and the process continues to the next node; (3b) If the retry fails, the third level intervention is triggered; (4) In the event that user intervention fails or there is no solution in the knowledge base, the third-level intervention invokes the expert processing mechanism. The MCP Server pushes the complete fault information of the current node to the industry expert. The expert investigates the root cause and performs repairs before initiating a retry: (4a) If the retry is successful, the expert processing log is recorded, including the root cause of the failure, the repair plan and the execution result, and the process continues to the next node; (4b) If the retry fails, the final failure status is recorded, a work order containing complete failure information is automatically generated and submitted to the operation and maintenance management system, and the current deployment process is terminated.

[0022] The database installation and deployment method based on the MCP protocol of the present invention has the following beneficial effects compared with the prior art: 1. This invention uses the MCP Server as its core and implements full-process automation through the MCP protocol and MCP Tools. Relying on natural language interaction and simplified configuration, it lowers the user threshold, allowing non-professional users to independently complete deployment. Through automated perception, decision-making, and execution, it increases the accuracy of machine information acquisition by over 90%, reduces manual intervention by 80%, and significantly improves deployment efficiency and universality. It is suitable for the rapid and easy-to-use deployment of databases in multiple scenarios. It solves the problems of traditional database automated deployment, such as high user expertise requirements, inefficient machine information perception, frequent manual intervention, and cumbersome privacy configuration. 2. This invention relies on the MCP Server to significantly reduce manual operation steps. Operators only need to enter privacy information and trigger instructions to complete the process, completely eliminating tedious operations such as manual configuration and dependency troubleshooting. Through natural language interaction, users do not need to master operating system and database knowledge, and even non-operation and maintenance professionals can achieve independent deployment. For users, the system automatically shields hardware architecture and operating system differences, enabling "idiot-proof" environment adaptation, greatly lowering the technical threshold. Database providers do not need to write customized scripts for different hardware and software environments. They only need to maintain a standardized knowledge base and prompt vocabulary, and use the universal MCP Server tool to quickly adapt to new scenarios. 3. The present invention achieves real-time perception and hierarchical processing of deployment anomalies by presetting judgment nodes in key links such as environment detection, dependency reinstallation, and configuration verification: triggering a built-in retry mechanism for known problems; generating structured interactive requests for complex problems, including environmental context information and recommended solutions, so that non-professional users can also make correct decisions; and supporting the resumption of the process from the current node after manual processing to avoid repeated operations. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Attachment Figure 1 is a flow chart of a method according to an embodiment of the present invention; Attachment Figure 2 4 is a three-level manual intervention flow chart of an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to make the technical solution, the technical problems solved and the technical effects of the present invention more clear, the technical solution of the present invention is clearly and completely described below in conjunction with specific embodiments.

[0025] The professional terms involved in the following embodiments are now explained.

[0026] MCP (Model Context Protocol) is a standardized protocol used to uniformly model, transmit, and analyze contextual information such as hardware environment, software dependencies, and security policies during database deployment.

[0027] MCP Server: A central control component based on the MCP protocol. As the decision-making center of the deployment process, it is responsible for receiving user instructions, scheduling MCP Tools to perform environmental testing, coordinating deployment tasks, and returning results.

[0028] MCP Tools: A lightweight, cross-platform toolset, called by the MCP Server, to collect target server hardware information (CPU architecture, memory / hard disk capacity), operating system (kernel version, installed software), and network status, providing data support for deployment decisions.

[0029] Natural language instructions: Deployment requirements expressed by users in everyday language (such as "Install a MySQL 8.0 cluster on the test server") are converted into executable deployment tasks through the MCP Server's natural language parsing engine.

[0030] Cross-platform compatibility: The system's ability to operate normally under different hardware architectures (such as x86, ARM), operating systems (such as Linux, Windows), and deployment forms (physical machines, virtual machines, cloud servers).

[0031] Database deployment: The complete process of converting database software from an installation package to an operational state, including steps such as environment preparation, software installation, parameter configuration, service startup, and cluster initialization.

[0032] Dependency reinstallation: Before database deployment, the system automatically detects missing essential software components (such as OpenSSL and Python runtime) in the target environment based on a specific method, and obtains compatible versions from the software source for installation.

[0033] Example: Refer to the attached Figure 1 This embodiment proposes a database installation and deployment method based on the MCP protocol, which includes the following steps: S1. Based on the MCP protocol, the standardized configuration of privacy information is realized, and users can complete the relevant settings of privacy information through the operation interface.

[0034] When executing step S1, the user enters the privacy information of the database deployment through the preset page or API in the standardized format configured by the MCP protocol, including server login credentials (IP, port and SSH key), administrator account password, data encryption certificate and key, network access whitelist (limiting the range of IP addresses allowed to connect) and data storage path and permissions (such as read and write permissions and user group configuration).

[0035] The privacy information entered by users through preset pages or APIs will serve as the core basis for security authentication, data encryption, and access control in the subsequent deployment process, ensuring that the deployment process complies with privacy protection and permission management requirements, while providing MCPServer with basic parameters for executing security policies.

[0036] S2. Leveraging the natural language parsing capabilities of the MCP protocol, the MCP Server receives natural language deployment instructions entered by the user and triggers the deployment process. The MCP Server, acting as the core processing hub, calls MCP Tools to collect the server's full environment information, matches the appropriate installation package version, and, based on the server's network status, determines whether the installation package needs to be transmitted online. The MCP Server then schedules the installation package.

[0037] During step S2, the user enters deployment requirements (e.g., "Deploy a PostgreSQL 14 master-slave cluster in a production environment") in natural language based on a preset page or API. The MCP Server calls the parsing rules in the prompt word library and uses the natural language engine to break down the key elements: database type (PostgreSQL), version (14), deployment form (master-slave cluster) and target environment (production environment). The prompt word library stores a set of mapping rules between natural language instructions and deployment tasks, which is used by the MCP Server's natural language parsing engine to identify user intent and generate corresponding deployment work orders. The MCP Server then retrieves deployment policy templates for the corresponding environment from the knowledge base (such as the high availability configuration and backup strategies required for production environments). Combined with the security parameters in the privacy information configuration, it generates standardized deployment work orders containing execution steps, resource requirements, and security constraints, translating the user's natural language requirements into automated tasks. The knowledge base, which stores the environment model, dependencies, and deployment strategies required for database deployment, serves as a crucial basis for the MCP Server's decision-making.

[0038] In step S2, the MCP Server, as the core processing hub, calls MCP Tools to collect all server environment information and match it to the appropriate installation package version. Specifically, the following steps are performed: After converting the user's natural language requirements into automated tasks, the MCP Server distributes the lightweight tool MCP Tools to the target server, triggering the collection of comprehensive environmental information. Hardware-level information includes CPU architecture (x86 / ARM), memory capacity (e.g., 16GB), storage type (SSD / HDD) and capacity; software-level information includes operating system version (e.g., CentOS 7.9), kernel parameters (e.g., memory page size), and installed software (to avoid version conflicts); and network-level information includes IP address, available ports (e.g., whether port 3306 is occupied), firewall rules (whether database port communication is allowed), and network bandwidth (to assess installation package transmission speed). After the collection is complete, the MCP Server compares the information with the environment model in the knowledge base (such as the "ARM architecture adaptation version list" and "CentOS 7 compatible deployment parameters"), and automatically selects the adapted installation packages (such as the MySQL installation package dedicated to the ARM architecture) and deployment parameters (such as adjusting the cache configuration according to the memory size) to ensure that subsequent deployment is compatible with the hardware and software characteristics of the target environment.

[0039] S3. Detect the dependency conditions of the target environment based on the selected installation package and automatically install the missing software packages; then automatically perform database installation, initialization configuration and monitoring component deployment.

[0040] This process specifically includes: S3.1. Based on the selected installation package, the MCP Server first checks whether the local cache exists. If so, it directly calls the package. If online access is required, it downloads it from a specified source (such as an official mirror site or a private enterprise repository) based on the network bandwidth parameters and installation package size in the environment model. Based on environment information (such as memory capacity and number of CPU cores) and configuration templates in the knowledge base, it automatically generates a database configuration file (such as MySQL's my.cnf file, which contains information such as cache size, connection limit, and log path). S3.2. For distributed clusters (such as MySQL InnoDB Cluster), each node service is automatically started, replica roles (master / slave) are assigned based on the node hardware performance, cluster handshake and data synchronization are completed through internal communication protocols, database services are activated, and initial accounts and permissions are created based on the administrator information in the privacy configuration (such as granting remote access rights to the root user), completing the deployment and activation of core database functions. S3.3. After the database is deployed, real-time monitoring of the database's operating status and operation and maintenance support are achieved through probe distribution, indicator collection, alarm strategy configuration, and visualization. The specific operations are as follows: S3.3.1. The MCP Server automatically distributes monitoring probes (such as the Prometheus exporter) to target servers. The probes collect key operational metrics in real time: resource utilization (CPU, memory, and disk I / O), database performance metrics (QPS, TPS, number of connections, and number of slow queries), and cluster status (master-slave synchronization latency and node health). S3.3.2. Configure the probe's alarm thresholds and notification methods (e.g., email, SMS, and monitoring platform interface) based on the alarm policy of the environment model in the knowledge base (e.g., "production environment memory usage > 90% triggers an emergency alarm" or "slave database synchronization delay > 30s triggers a warning"). S3.3.3. Connect the probe data to a visual monitoring platform (such as Grafana) to generate a real-time dashboard that visually displays the database operation status and provides data support for subsequent operations and maintenance.

[0041] S4: The MCP Server summarizes the status information of the entire process and returns the deployment results, including: S4.1. The MCP Server uses log collection tools to aggregate the execution data of the preceding steps, including timelines (e.g., "Dependency detection took 2 minutes" or "Cluster initialization took 5 minutes"), environment information summaries (e.g., hardware configuration and operating system version), configuration parameter details (e.g., key items in the configuration file), manual interaction records (if any temporary intervention was required), and execution results (success / failure and the reason). S4.2. Generate a structured deployment report based on the summarized data and provide feedback through user-specified channels (such as web pages, emails, and API callbacks). The deployment report contains the final status of the deployment results and detailed logs of each link (to facilitate problem tracing), marking the closed loop of the entire process from "user request" to "deployment completion" and providing data basis for subsequent optimization of deployment strategies.

[0042] It should be added that MCP Tools, as a lightweight cross-platform tool component, automatically collects the full environmental information of the server, including hardware information (CPU architecture, memory / storage capacity), operating system fingerprint (kernel version, installed software list) and network status; The MCP Server serves as the decision-making hub. Based on the MCP protocol's standardized privacy information configuration format, it interprets user instructions (such as "Install a MySQL cluster on the 192.168.123.101 server") through a natural language interactive interface, automatically triggering the database installation and deployment process. At the same time, a unified environment model is defined through the MCP protocol, and the context parameters of hardware, software, and security policies are standardized, enabling the MCP Server to dynamically adjust deployment strategies based on real-time environment information (such as skipping the installation of high-memory consumption modules when the memory is less than 8GB, and loading the corresponding version installation package when the ARM architecture is detected), achieving non-discriminatory adaptation across cross-platform environments.

[0043] Based on the deployment method of the embodiment, it should be supplemented that an intelligent decision-making assistance AI module is preset, which integrates the historical fault processing knowledge base, process node execution rules and environmental parameter analysis model to provide automated decision support during the intervention process.

[0044] During the execution of steps S1-S4, when encountering key points or unexpected stuck points, three levels of manual intervention are supported. Figure 2 The specific intervention process is as follows: (1) During the execution of each node in the installation and deployment process, the node operation status is monitored in real time. When an execution failure or timeout is detected in the current node, a first-level intervention is immediately triggered; if no abnormality is detected, the process automatically proceeds to the next node; (2) For transient problems such as temporary network fluctuations or file lock conflicts that can be recovered through retries, the first-level intervention automatically calls the AI ​​retry mechanism and re-executes the current node operation according to the rule of "10-second interval, 3 times by default (the retry interval and number can be adjusted through the configuration file)": (2a) If the retry is successful, the AI ​​retry log is recorded (including the failed node ID, first failure time, number of retries and recovery time), and the process seamlessly connects to the next node; (2b) If the attempt fails after three retries, a secondary intervention is triggered; (3) When the AI ​​retry mechanism fails and there is a matching solution in the knowledge base, the secondary intervention invokes the user intervention retry mechanism. The intelligent decision-making auxiliary AI module generates options containing "card point type + environmental information + 2-3 recommended operations" (such as "Port 3306 is occupied: ① Automatically switch to 3307, ② Manually specify the port, ③ Terminate") and pushes the option information to the user. The user selects the operation and manually initiates a retry: (3a) If the retry is successful, the user intervention log is recorded, including the user operation content and retry result, and the process continues to the next node; (3b) If the retry fails, the third level intervention is triggered; (4) In the event that user intervention fails or there is no solution in the knowledge base, the third-level intervention calls the expert processing mechanism. The MCP Server pushes the complete fault information of the current node (full log, error stack and system snapshot) to the industry expert. The expert investigates the root cause and performs repairs (such as driver adaptation and kernel parameter adjustment) before initiating a retry: (4a) If the retry is successful, the expert processing log is recorded, including the root cause of the failure, the repair plan and the execution result, and the process continues to the next node; (4b) If the retry fails, the final failure status is recorded, a work order containing complete failure information is automatically generated and submitted to the operation and maintenance management system, and the current deployment process is terminated.

[0045] The first, second, and third levels of intervention are executed sequentially, and the next level of intervention is triggered only after the current level of intervention fails. The order of execution of the first, second, and third levels of intervention follows the principle of "step-by-step progression," forming a stepped troubleshooting chain of "automatic repair → user intervention → expert handling," ensuring that problems are addressed at the appropriate level according to their severity.

[0046] In summary, the MCP protocol-based database installation and deployment method of the present invention uses the MCP Server as the core and implements full-process automation through the MCP protocol and MCP Tools. Natural language interaction and simplified configuration lower the user threshold, allowing non-professional users to independently complete database installation and deployment. This method solves the problems of traditional database automated deployment, such as high user expertise requirements, inefficient machine information perception, frequent manual intervention, and cumbersome privacy configuration.

[0047] The above specific examples are used to illustrate the principles and implementation methods of the present invention in detail. These examples are only used to help understand the core technical content of the present invention. Based on the above specific embodiments of the present invention, any improvements and modifications made by those skilled in the art without departing from the principles of the present invention should fall within the scope of patent protection of the present invention.

Claims

1. A database installation and deployment method based on the MCP protocol, characterized in that: The steps include: S1. Implement standardized configuration of privacy information based on the MCP protocol, allowing users to complete relevant settings of privacy information through the operation interface; S2. Leveraging the MCP protocol's natural language parsing capabilities, the MCP Server receives natural language deployment instructions entered by the user and triggers the deployment process. The MCP Server, acting as the core processing hub, calls MCP Tools to collect the server's full environment information, matches the appropriate installation package version, and, based on the server's network status, determines whether the installation package needs to be transmitted online. The MCP Server then schedules the installation package. S3. Detect the target environment's dependency conditions based on the selected installation package and automatically install any missing software packages; then automatically perform database installation, initial configuration, and monitoring component deployment. S4. The MCP Server summarizes the status information of the entire process and returns the deployment result.

2. A database installation and deployment method based on the MCP protocol according to claim 1, characterized in that: The MCP Tools, as a lightweight cross-platform tool component, automatically collects the full environmental information of the server; The MCP Server serves as the decision-making hub, standardizes the privacy information configuration format based on the MCP protocol, parses user instructions through a natural language interactive interface, and automatically triggers the database installation and deployment process; At the same time, a unified environment model is defined through the MCP protocol, and the context parameters of hardware, software, and security policies are standardized, enabling the MCP Server to dynamically adjust deployment strategies based on real-time environmental information and achieve non-discriminatory adaptation across cross-platform environments.

3. A database installation and deployment method based on the MCP protocol according to claim 1, characterized in that: In step S1, the user enters the privacy information of the database deployment through a preset page or API in a standardized format configured by the MCP protocol, including server login credentials, administrator account password, data encryption certificate and key, network access whitelist, and data storage path and permissions; The privacy information entered by users through preset pages or APIs will serve as the core basis for security authentication, data encryption, and access control in the subsequent deployment process, ensuring that the deployment process meets privacy protection and permission management requirements, while providing the basic parameters for the MCP Server to execute security policies.

4. The database installation and deployment method based on the MCP protocol according to claim 3, characterized in that: When executing step S2, based on a preset page or API, the user inputs deployment requirements in natural language; The MCP Server calls the parsing rules in the prompt vocabulary and uses the natural language engine to break down the key elements: database type, version, deployment form, and target environment; Subsequently, the MCP Server retrieves the deployment policy template for the corresponding environment from the knowledge base, combines it with the security parameters in the privacy information configuration, and generates a standardized deployment work order containing execution steps, resource requirements, and security constraints, converting the user's natural language requirements into automated tasks.

5. The database installation and deployment method based on the MCP protocol according to claim 4 is characterized in that: The prompt word library stores a set of natural language instructions and deployment task mapping rules, which are used by the natural language parsing engine of the MCP Server to identify user intentions and generate corresponding deployment work orders; The knowledge base stores the environment model, dependency relationships, and deployment strategies required for database deployment, and is an important basis for MCP Server decision-making.

6. A database installation and deployment method based on the MCP protocol according to claim 5, characterized in that: In step S2, the MCP Server, as the core processing hub, calls MCP Tools to collect all server environment information and match it to the appropriate installation package version. Specifically, the following steps are performed: After converting the user's natural language requirements into automated tasks, the MCP Server distributes the lightweight tool MCP Tools to the target server, triggering the collection of full environmental information. Hardware information includes CPU architecture, memory capacity, storage type and capacity; software information includes operating system version, kernel parameters, and installed software; and network information includes IP address, available ports, firewall rules, and network bandwidth. After the collection is completed, the MCP Server compares the information with the environment model in the knowledge base and automatically selects the appropriate installation package and deployment parameters to ensure that subsequent deployment is compatible with the hardware and software characteristics of the target environment.

7. The MCP-based database installation and deployment method according to claim 6, wherein: The step S3 specifically includes: S3.

1. Based on the selected installation package, the MCP Server first checks whether the local cache exists. If so, it directly calls the package. If online access is required, it is downloaded from the specified source as needed based on the network bandwidth parameters and installation package size in the environment model. The database configuration file is automatically generated based on the environment information and the configuration template in the knowledge base. S3.

2. For distributed clusters, automatically start each node service, assign replica roles based on node hardware performance, complete cluster handshake and data synchronization through internal communication protocols, activate database services, and create initial accounts and permissions based on administrator information in the privacy configuration, completing the deployment and activation of core database functions. S3.

3. After the database is deployed, real-time monitoring of the database's operating status and operation and maintenance support are achieved through probe distribution, indicator collection, alarm strategy configuration, and visual display.

8. The database installation and deployment method based on the MCP protocol according to claim 7, characterized in that: The step S3.3 specifically includes: S3.3.

1. The MCP Server automatically distributes monitoring probes to target servers. The probes collect key operational indicators in real time: resource utilization, database performance indicators, and cluster status. S3.3.

2. Configure the probe's alarm threshold and notification method based on the alarm strategy of the environment model in the knowledge base; S3.3.

3. Connect the probe data to the visual monitoring platform to generate a real-time dashboard to intuitively display the database operation status and provide data support for subsequent operation and maintenance.

9. The database installation and deployment method based on the MCP protocol according to claim 7, characterized in that: The step S4 specifically includes: S4.

1. The MCP Server uses log collection tools to aggregate the execution data from the preceding steps, including timelines, environment information summaries, configuration parameter details, manual interaction records, and execution results. S4.

2. Generate a structured deployment report based on the summarized data and provide feedback through the user's specified channels. The deployment report contains the final status of the deployment results and detailed logs of each link, marking the closed loop of the entire process from "user request" to "deployment completion" and providing data basis for subsequent optimization of deployment strategies.

10. The database installation and deployment method based on the MCP protocol according to claim 1, characterized in that: A pre-set intelligent decision-making support AI module integrates the historical fault handling knowledge base, process node execution rules, and environmental parameter analysis models to provide automated decision support during the intervention process; During steps S1-S4, if a critical point or unexpected stuck point is encountered, three levels of manual intervention are supported. The specific intervention process is as follows: (1) During the execution of each node in the installation and deployment process, the node operation status is monitored in real time. When an execution failure or timeout is detected in the current node, a first-level intervention is immediately triggered; if no abnormality is detected, the process automatically proceeds to the next node; (2) For transient problems such as temporary network fluctuations or file lock conflicts that can be recovered through retries, the first-level intervention automatically calls the AI ​​retry mechanism driven by the intelligent decision-making assistance AI module to re-execute the current node operation according to the preset rules: (2a) If the retry is successful, the AI ​​retry log is recorded, including the failed node ID, the time of the first failure, the number of retries, and the recovery time, and the process seamlessly connects to the next node; (2b) If the attempt fails after the number of retries specified in the pre-set rules, a secondary intervention is triggered; (3) When the AI ​​retry mechanism fails and a matching solution exists in the knowledge base, the secondary intervention invokes the user intervention retry mechanism. The intelligent decision-making assistance AI module generates options containing "stuck point type + environmental information + up to 3 recommended actions" and pushes the option information to the user. The user selects an action and manually initiates a retry: (3a) If the retry is successful, the user intervention log is recorded, including the user operation content and retry result, and the process continues to the next node; (3b) If the retry fails, the third level intervention is triggered; (4) In the event that user intervention fails or there is no solution in the knowledge base, the third-level intervention invokes the expert processing mechanism. The MCP Server pushes the complete fault information of the current node to the industry expert. The expert investigates the root cause and performs repairs before initiating a retry: (4a) If the retry is successful, the expert processing log is recorded, including the root cause of the failure, the repair plan and the execution result, and the process continues to the next node; (4b) If the retry fails, the final failure status is recorded, a work order containing complete failure information is automatically generated and submitted to the operation and maintenance management system, and the current deployment process is terminated.

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