An AI marketing cross-system automatic calling method and device based on an MCP protocol

Abstract

This invention provides a method for automatic cross-system invocation of AI marketing based on the MCP protocol, comprising the following steps: receiving marketing task instructions generated by the AI ​​marketing system; parsing the marketing task instructions, extracting task elements and labeling them with scene tags; matching available tools from the MCP registered tool library based on the scene tags and task type; performing permission verification and security verification; invoking the available tools according to the standardized format of the MCP protocol by calling the execution engine; collecting the execution results of each tool and generating an execution report in a unified format; and feeding the execution report back to the AI ​​marketing system. This invention provides a method for automatic cross-system invocation of AI marketing based on the MCP protocol, which has a high degree of standardization and reduces integration costs: it extends marketing-specific standards on the basis of the standard MCP protocol, unifies the task description format, communication protocol, and data format, and solves the API fragmentation problem.

Description

Technical Field

[0001] This invention relates to the field of artificial intelligence marketing, and in particular to a method and apparatus for automatic cross-system invocation of AI marketing based on the MCP protocol. Background Technology

[0002] With the deepening application of artificial intelligence technology in the marketing field, modern marketing systems need to integrate a variety of tools and services, such as customer relationship management, marketing automation platforms, advertising delivery systems, content management systems, and data analysis tools.

[0003] However, existing technologies suffer from issues such as API fragmentation, insufficient real-time performance, lack of context, and low standardization. The standard MCP protocol, while designed for general use, is not optimized for the specific needs of marketing scenarios. Specifically, it lacks marketing-specific task description standards and parsing mechanisms, does not support tag-based scheduling for vertical scenarios such as hotels, pharmacies, and business travel, lacks dynamic priority management mechanisms based on the urgency of marketing tasks, and is not deeply customized for marketing scenarios in terms of real-time response, error handling, and access control. As a result, it cannot adapt to the personalized and high real-time requirements of marketing business, leading to low integration efficiency, poor execution results, and high operation and maintenance costs for marketing systems.

[0004] Therefore, it is necessary to provide an AI marketing cross-system automatic invocation method based on the MCP protocol to solve the above-mentioned technical problems. Summary of the Invention

[0005] This invention provides an AI marketing cross-system automatic invocation method based on the MCP protocol, which solves the problems of API fragmentation, insufficient real-time performance, missing context, low standardization, and the fact that the standard MCP protocol is not adapted to the needs of marketing scenarios, resulting in complex integration, low execution efficiency, and poor scalability in cross-tool invocation of existing marketing systems.

[0006] To address the aforementioned technical problems, this invention provides an AI marketing cross-system automatic invocation method based on the MCP protocol, comprising the following steps: S1. Receive marketing task instructions generated by the AI ​​marketing system; S2. Parse the marketing task instructions, extract task elements, and label them with scene tags; S3. Based on the scene tags and task types, match available tools from the MCP registration tool library; S4. Perform permission verification and security checks; S5. By invoking the execution engine, the available tools are invoked in accordance with the standardized format of the MCP protocol; S6. Collect the execution results of each tool and generate an execution report in a unified format; S7. Feed the execution report back to the AI ​​marketing system.

[0007] Preferably, the task elements in S2 include at least one of target audience, marketing channels, triggering conditions, and priority; The scenario tags include at least one of the following: hotel, pharmacy, business travel, retail, and education.

[0008] Preferably, the call execution engine in S5 supports at least one of the call modes of synchronous call, asynchronous call and streaming call, and has built-in retry mechanism, timeout control and error handling functions.

[0009] Preferably, the semantic similarity calculation and weight matching algorithm in S3 dynamically selects the optimal tool combination and supports hot-swapping and dynamic expansion of tools.

[0010] Preferably, the unified format execution report in S6 includes at least one of the following: structured data, unstructured content, execution status, error information, reach rate, and conversion rate prediction.

[0011] An AI marketing cross-system automatic invocation device based on the MCP protocol includes: a task parsing module, a tool library index module, an invocation execution engine, and a result feedback module; The task parsing module is used to receive AI marketing task instructions, extract task elements and label scene tags, and generate standardized MCP call requests. The tool library index module is used to maintain the MCP registration tool metadata database and supports intelligent tool matching based on scene tags and task types; The call execution engine is used to execute standardized calls to the MCP protocol and supports multiple call modes. The results feedback module is used to collect the execution results of each tool, generate execution reports in a unified format, and feed them back to the AI ​​marketing system.

[0012] Preferably, the task parsing module has a built-in marketing task parsing standard, which defines a standardized task description format including target audience, marketing channels, triggering conditions, and priority. The tool library index module supports dynamic discovery and version management of tool capabilities and maintains the mapping relationship between scene tags and tool capabilities.

[0013] Preferably, the execution engine includes a parallel call optimization unit, which distributes parallelizable subtasks to multiple tools for execution simultaneously through task decomposition and dependency analysis. The result feedback module includes a data cleaning unit, a format conversion unit, and an effect analysis unit, which perform multi-dimensional processing and analysis on the execution results.

[0014] Preferably, the tool library index module uses a network device, which includes a router body, a disassembly base plate, two fixing components, a U-shaped groove, a heat dissipation mesh, and a fixing plate. The disassembly base plate is located at the bottom of the router body. The two fixing components are respectively located between the disassembly base plate and the router body. Each fixing component includes an L-shaped fixing block, an external threaded block, and a threaded sleeve. The L-shaped fixing block is connected to the end of the disassembly base plate, the external threaded block is connected to the side of the router body, and the threaded sleeve is threaded to the surface of the external threaded block. The U-shaped groove is formed on the surface of the disassembly base plate, the heat dissipation mesh is located inside the U-shaped groove, and the fixing plate is located inside the U-shaped groove and on one side of the heat dissipation mesh.

[0015] Preferably, a slot adapted to the L-shaped fixing block is provided on one side of the router body, and sliding grooves are provided on both the left and right sides of the inner wall of the U-shaped groove. Multiple sliders are slidably connected inside the two sliding grooves, and the multiple sliders are respectively connected to the heat dissipation mesh and the two sides of the fixing plate.

[0016] Compared with related technologies, the AI ​​marketing cross-system automatic invocation method based on the MCP protocol provided by this invention has the following beneficial effects: This invention provides an AI marketing cross-system automatic invocation method based on the MCP protocol, which has a high degree of standardization and reduces integration costs: it extends the marketing-specific standard on the basis of the standard MCP protocol, unifies the task description format, communication protocol and data format, solves the API fragmentation problem, avoids vendor lock-in, and shortens the access time of new tools from several weeks to several hours. At the same time, the standardized installation and maintenance process of dedicated network devices reduces the operation and maintenance costs of hardware deployment. Fast response speed to meet real-time requirements: Through task decomposition and dependency analysis, multiple tools can be called in parallel. Combined with dynamic priority scheduling and AI algorithm acceleration, the response speed of marketing tasks is improved from several minutes for manual operation to seconds for automatic operation, meeting the real-time requirements of modern marketing. The efficient heat dissipation design of the network device ensures continuous high-speed data transmission, further improving the overall response efficiency. Deepening the level of intelligence enhances marketing effectiveness: The intelligent tool selection mechanism based on semantic similarity and weight matching, combined with personalized calls based on user context information, improves marketing accuracy; the multi-dimensional effect analysis and closed-loop optimization of the results feedback module help the AI ​​marketing system continuously iterate its strategies, and is expected to significantly improve conversion rate and ROI. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of a preferred embodiment of an AI marketing cross-system automatic invocation method based on the MCP protocol provided by the present invention. Figure 2A schematic diagram of the structure of a first embodiment of an AI marketing cross-system automatic invocation device based on the MCP protocol provided by the present invention; Figure 3 A schematic diagram of the structure of a second embodiment of an AI marketing cross-system automatic invocation device based on the MCP protocol provided by the present invention; Figure 4 for Figure 3 The enlarged schematic diagram of part A shown below; Figure 5 for Figure 3 The enlarged schematic diagram of section B is shown below; Figure 6 for Figure 3 The diagram shows a first-person perspective 3D structural representation of the network device. Figure 7 for Figure 6 The enlarged schematic diagram of section C is shown below; Figure 8 for Figure 6 The enlarged schematic diagram of part D is shown below; Figure 9 for Figure 3 The diagram shows a three-dimensional structure of the network device from a second-view perspective. Figure 10 for Figure 3 The diagram shows a three-dimensional structure of the network device from a third-person perspective.

[0018] The diagram is labeled: 1. Router main body; 2. Removing the bottom plate. 3. Fixing components; 31. L-shaped fixing block; 32. External threaded block; 33. Threaded sleeve. 4. Card slot, 5. U-shaped groove, 6. Heat dissipation mesh, 7. Fixing plate, 8. Slider, 9. Slide groove. Detailed Implementation

[0019] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0020] First Embodiment Please refer to the following: Figure 1 ,in, Figure 1 This is a schematic diagram of a preferred embodiment of an AI marketing cross-system automatic invocation method based on the MCP protocol provided by the present invention. The AI ​​marketing cross-system automatic invocation method based on the MCP protocol includes the following steps:

[0021] S1. Receive marketing task instructions generated by the AI ​​marketing system; S2. Parse the marketing task instructions, extract task elements, and label them with scene tags; S3. Based on the scene tags and task types, match available tools from the MCP registration tool library; S4. Perform permission verification and security checks; S5. By invoking the execution engine, the available tools are invoked in accordance with the standardized format of the MCP protocol; S6. Collect the execution results of each tool and generate an execution report in a unified format; S7. Feed the execution report back to the AI ​​marketing system.

[0022] The task elements in S2 include at least one of the following: target audience, marketing channels, triggering conditions, and priority. The scenario tags include at least one of the following: hotel, pharmacy, business travel, retail, and education.

[0023] The call execution engine in S5 supports at least one of the following call modes: synchronous call, asynchronous call, and streaming call, and has built-in retry mechanism, timeout control, and error handling functions.

[0024] The S3 algorithm, based on semantic similarity calculation and weight matching, dynamically selects the optimal tool combination and supports hot-swapping and dynamic expansion of tools.

[0025] The unified format execution report in S6 includes at least one of the following: structured data, unstructured content, execution status, error information, reach rate, and conversion rate prediction.

[0026] Task reception: The AI ​​marketing system generates marketing task instructions in natural language form: "Push nearby restaurant discount information to members located in this city who stayed at this hotel last month and prefer Chinese food, with high priority."

[0027] Task parsing: After receiving the above instructions, the task parsing module parses the instructions and extracts entities and intents through the Natural Language Understanding (NLU) module. After the rule engine verifies the completeness and legality of the task elements, it extracts the core task elements: the target audience is hotel members in the city who stayed last month and prefer Chinese food; the marketing channel is push notifications; the trigger condition is that the member's location is within the city; and the priority is high. At the same time, the scene tag classifier labels the task with three levels of scene tags: hotel, catering, and geolocation service based on keyword and semantic matching.

[0028] Tool Matching and Validation: After receiving a standardized MCP call request, the tool library index module performs tool matching from the MCP registered tool library based on hotel, catering, and location service scenario tags and promotional task types. First, the task description and tool capability description are converted into semantic vectors using a BERT model. Cosine similarity is calculated and tools with similarity below 0.7 are filtered out. Then, a weighted matching algorithm (scenario tag matching weight 0.4, task type matching weight 0.3, historical success rate weight 0.2, response time weight 0.1) is used to calculate a comprehensive score. Finally, four usable tools are matched: CRM system (filtering target members), location service (determining the real-time location of members), POS system (obtaining discount information from nearby catering merchants), and push service platform (sending personalized push notifications). After tool matching is completed, the device performs permission verification and security verification on the above four tools in sequence: verifying the current user's access rights to the tools according to the role permission matrix and checking the data permission boundaries; at the same time, verifying the validity of the API key and JWT token, checking the request frequency limit and anti-attack rules, and entering the call execution stage after the verification is passed.

[0029] Parallel execution calls: After receiving the list of matching tools, the execution engine decomposes the marketing task into four atomic subtasks, constructs a dependency graph (DAG) without dependencies, and determines that all subtasks can be executed in parallel. Then, in accordance with the standardized format of the MCP protocol, it initiates call requests to the four tools in parallel in an asynchronous call mode. Each request's extended header X-MCP-Context carries JWT-encrypted session context information. The specific execution operations are as follows: calling the MCP interface of the CRM system to filter the list of members that meet the conditions based on the user profile information of the context; calling the MCP interface of the geolocation service to perform initial location filtering based on the user_profile.demographics.city field of the context to obtain the member's real-time location; calling the MCP interface of the POS system to query nearby restaurants and corresponding discount information based on the temporal_context.time_of_day (lunchtime) information of the context; and calling the MCP interface of the push service platform to compile and send personalized push content based on the user_profile.preferences (preference for Chinese food) information of the context.

[0030] During the call, the execution engine monitors each subtask in real time. The built-in fault tolerance mechanism configures a maximum of 3 retries and a custom timeout threshold for each subtask. In this embodiment, all subtasks were executed successfully without any retries or timeouts.

[0031] Context update: After each tool completes its execution, it returns a context update fragment. The device adopts an incremental update and version merging mechanism, and uses optimistic locking and conflict detection algorithms to merge the update fragments into the main context to ensure context consistency.

[0032] The updated context data is stably transmitted to the result feedback module via the network device.

[0033] Results Aggregation and Feedback: The results feedback module collects the raw execution results of each tool, specifically as follows: the CRM system identified 1234 target members; the geolocation service successfully obtained the real-time locations of 987 members; the POS system obtained discount information from 32 nearby restaurants; and the push service platform successfully sent 876 push notifications, with 58 failing. The results feedback module performs data cleaning (removing duplicate records and standardizing data format), format conversion (adapting to a unified reporting standard), and performance analysis (calculating business metrics) on the above raw results, generating a unified format MCP execution report. The report includes four core modules: task summary, tool-specific execution details, performance metrics, and business results. The reach rate was 71.1% (876 / 1234), and the conversion rate and ROI of this marketing task were predicted using a machine learning model based on historical data. Simultaneously, performance metrics such as average response time, throughput, and success rate of this call were statistically analyzed, and error codes and reasons for failed pushes were recorded. The final results feedback module pushes the execution report to the AI ​​marketing system in real time via network devices. The AI ​​marketing system optimizes its strategies based on the execution results in the report, such as adjusting the target audience selection criteria, optimizing the push time, and using SMS channels to re-reach members who failed to receive pushes, thus forming a marketing closed loop.

[0034] The working principle of the AI ​​marketing cross-system automatic invocation method based on the MCP protocol provided by this invention is as follows: The core of this invention, an AI marketing cross-system automatic invocation method based on the MCP protocol, addresses the fragmentation and inefficiency issues of cross-system marketing invocation through standardized protocols and intelligent mechanisms. First, it receives task instructions from the AI ​​marketing system, uses natural language understanding and a rule engine to parse out core elements such as the target audience and channels, labels the requests with scene tags, and generates standardized MCP requests containing context. Then, based on the scene tags and task type, it selects the optimal tool from the tool library using semantic similarity calculation and weight matching algorithms. After permission verification and security checks, the invocation execution engine decomposes complex tasks into atomic subtasks, identifies parallelizable tasks through DAG dependency analysis, and invokes external tools in multiple modes according to the MCP protocol. Finally, it collects the tool execution results, cleans the data, converts the format, and analyzes the effects to generate a unified report, which is then fed back to the AI ​​marketing system to form a closed-loop optimization. The entire process requires no manual intervention and ensures efficient and stable execution of marketing tasks through parallel invocation, dynamic priority scheduling, and fault tolerance mechanisms.

[0035] Compared with related technologies, the AI ​​marketing cross-system automatic invocation method based on the MCP protocol provided by this invention has the following beneficial effects: This invention provides an AI marketing cross-system automatic invocation method based on the MCP protocol, which has a high degree of standardization and reduces integration costs: it extends the marketing-specific standard on the basis of the standard MCP protocol, unifies the task description format, communication protocol and data format, solves the API fragmentation problem, avoids vendor lock-in, and shortens the access time of new tools from several weeks to several hours. At the same time, the standardized installation and maintenance process of dedicated network devices reduces the operation and maintenance costs of hardware deployment. Fast response speed to meet real-time requirements: Through task decomposition and dependency analysis, multiple tools can be called in parallel. Combined with dynamic priority scheduling and AI algorithm acceleration, the response speed of marketing tasks is improved from several minutes for manual operation to seconds for automatic operation, meeting the real-time requirements of modern marketing. The efficient heat dissipation design of the network device ensures continuous high-speed data transmission, further improving the overall response efficiency. Deepening the level of intelligence enhances marketing effectiveness: The intelligent tool selection mechanism based on semantic similarity and weight matching, combined with personalized calls based on user context information, improves marketing accuracy; the multi-dimensional effect analysis and closed-loop optimization of the results feedback module help the AI ​​marketing system continuously iterate its strategies, and is expected to significantly improve conversion rate and ROI.

[0036] Second Embodiment Please refer to the following: Figure 4 Based on the first embodiment of this application, an AI marketing cross-system automatic invocation device based on the MCP protocol is provided. The second embodiment of this application proposes another AI marketing cross-system automatic invocation device based on the MCP protocol. The second embodiment is merely a preferred embodiment of the first embodiment, and its implementation will not affect the separate implementation of the first embodiment.

[0037] Specifically, the second embodiment of this application provides an AI marketing cross-system automatic invocation device based on the MCP protocol, which differs in that it also includes a task parsing module, a tool library index module, an invocation execution engine, and a result feedback module; The task parsing module is used to receive AI marketing task instructions, extract task elements and label scene tags, and generate standardized MCP call requests. The tool library index module is used to maintain the MCP registration tool metadata database and supports intelligent tool matching based on scene tags and task types; The call execution engine is used to execute standardized calls to the MCP protocol and supports multiple call modes. The results feedback module is used to collect the execution results of each tool, generate execution reports in a unified format, and feed them back to the AI ​​marketing system.

[0038] The task parsing module has a built-in marketing task parsing standard, which defines a standardized task description format that includes target audience, marketing channels, triggering conditions, and priority. The tool library index module supports dynamic discovery and version management of tool capabilities and maintains the mapping relationship between scene tags and tool capabilities.

[0039] The execution engine includes a parallel call optimization unit, which distributes parallelizable subtasks to multiple tools for execution simultaneously through task decomposition and dependency analysis. The result feedback module includes a data cleaning unit, a format conversion unit, and an effect analysis unit, which perform multi-dimensional processing and analysis on the execution results.

[0040] Hardware environment configuration: The following basic hardware configuration requirements must be met, and the system can be flexibly expanded according to the scale of business operations: CPU: 4 cores or more; Memory: 8GB or more; Storage: 100GB or more of available space for storing the MCP registration tool metadata database, marketing task data, context data, execution reports, etc. Network: Stable internet connection with a bandwidth of no less than 10Mbps to ensure the stability of cross-system data transmission and heat dissipation efficiency.

[0041] Software environment configuration: Operating system: compatible with Linux (Ubuntu 20.04+), Windows Server 2019+, macOS 12+; Operating environment: Python 3.8+, with optional Node.js 16+; Database: Supports relational databases such as MySQL 8.0+ and PostgreSQL 13+, or non-relational databases such as MongoDB 5.0+, used to maintain the metadata database of the MCP registration tool; Container support: Optional Docker 20.10+ and Kubernetes 1.24+ enable lightweight deployment, elastic scaling and cluster management of the device.

[0042] Tool Registration: External marketing tools must first provide a server-side implementation that conforms to the MCP protocol, and then complete the registration through the RESTful API endpoint provided by the device. During registration, tool metadata must be submitted, including tool ID, name, version, capability description, scenario tag, interface URL, authentication method, output data schema, and other information. The device supports automatic discovery and dynamic registration of tools. After a new tool is registered, it is automatically added to the tool matching pool without restarting the device, and the registration data is securely transmitted and stored through a dedicated network device.

[0043] Security configuration: Configure SSL / TLS certificates to ensure encrypted MCP protocol communication between the device and external tools; Set up multiple authentication mechanisms such as API keys and JWT tokens to verify the legitimacy of the caller's identity; Define a role-based permission matrix to finely control different users' access permissions and data operation permissions to tools; Enable operation logs and audit trails to record all cross-system call operations, enabling behavior tracing and anomaly detection.

[0044] The working principle of the AI ​​marketing cross-system automatic invocation device based on the MCP protocol provided by this invention is as follows: Task parsing module: Built-in marketing task parsing standard. After receiving instructions, it completes element extraction, label annotation and standardized MCP request generation through NLU module, rule engine and scene label classifier, providing a unified input for subsequent calls; Tool library index module: Maintains the tool metadata database, supports dynamic tool discovery and version management, accurately filters available tools through intelligent matching algorithms, and coordinates with dedicated network devices to ensure data transmission stability; Call execution engine: Includes a parallel call optimization unit, which realizes parallel calls of multiple tools through task decomposition, dependency analysis and dynamic scheduling, and has built-in fault tolerance mechanisms such as retry and timeout control to adapt to the call requirements of different marketing scenarios; Results Feedback Module: Processes raw results through three main units: data cleaning, format conversion, and effect analysis, generating a unified report containing performance indicators and business results, realizing a closed loop of quantitative evaluation of marketing effectiveness and strategy optimization; Dedicated network device: Through the stable fixing of the router body and the disassembly base plate, the efficient heat dissipation of the heat dissipation mesh, and the convenient disassembly and assembly structure of the slider and the slide groove, the device ensures the stability, security and maintainability of data transmission between the device and external tools and systems.

[0045] Compared with related technologies, the AI ​​marketing cross-system automatic invocation device based on the MCP protocol provided by this invention has the following beneficial effects: This invention provides an AI marketing cross-system automatic invocation device based on the MCP protocol, which is highly scalable and adaptable to diverse scenarios: the modular design supports hot-swappable tools and dynamic expansion, and can be adapted to multiple vertical industries such as hotels, pharmacies, and business travel through scenario tag expansion, as well as diverse scenarios such as A / B testing and real-time triggered marketing, without the need to reconstruct the core architecture, and has a strong ability to adapt to business changes. High security and stability, ensuring business compliance: Inheriting the MCP security framework, it adds control logic such as data permission verification and compliance review specific to marketing scenarios. With the cooperation of SSL / TLS encryption, JWT authentication and other mechanisms, it ensures the security of data transmission and operation. The robust structure, efficient heat dissipation and convenient maintenance of the dedicated network device improve the overall system's operational stability and maintainability, and reduce the risk of business interruption.

[0046] Third Embodiment Please refer to the following: Figure 3 , Figure 4 , Figure 5 , Figure 6 , Figure 7 , Figure 8 , Figure 9 and Figure 10 Based on the first embodiment of this application, an AI marketing cross-system automatic invocation device based on the MCP protocol is provided. The third embodiment of this application proposes another AI marketing cross-system automatic invocation device based on the MCP protocol. The third embodiment is merely a preferred embodiment of the first embodiment, and its implementation will not affect the separate implementation of the first embodiment.

[0047] Specifically, the third embodiment of this application provides an AI marketing cross-system automatic invocation device based on the MCP protocol, which differs in that the tool library index module uses a network device. The network device includes a router body 1, a disassembly base plate 2, two fixing components 3, a U-shaped groove 5, a heat dissipation mesh 6, and a fixing plate 7. The disassembly base plate 2 is located at the bottom of the router body 1. The two fixing components 3 are respectively located between the disassembly base plate 2 and the router body 1. The fixing component 3 includes an L-shaped fixing block 31, an external threaded block 32, and a threaded sleeve 33. The L-shaped fixing block 31 is connected to the end of the disassembly base plate 2. The external threaded block 32 is connected to the side of the router body 1. The threaded sleeve 33 is threaded to the surface of the external threaded block 32. The U-shaped groove 5 is opened on the surface of the disassembly base plate 2. The heat dissipation mesh 6 is located inside the U-shaped groove 5. The fixing plate 7 is located inside the U-shaped groove 5 and on one side of the heat dissipation mesh 6.

[0048] The shape of the disassembly base plate 2 is adapted to the shape of the router body 1. A U-shaped fixing groove is provided on the L-shaped fixing block 31 to facilitate the connection between the L-shaped fixing block 31 and the external thread block 32. The use of two sliding grooves 9 and multiple sliders 8 facilitates the installation and removal of the heat dissipation mesh 6 and the fixing plate 7 from the disassembly base plate 2. When disassembling the heat dissipation mesh 6, first separate the disassembly base plate 2 from the router body 1 through the fixing component 3, and then pull the fixing plate 7 to drive the sliders 8 on both sides to separate from the two sliding grooves 9. After the fixing plate 7 is removed, pull the heat dissipation mesh 6 to drive the sliders 8 on both sides to separate from the two sliding grooves 9, so that the dust on the surface of the heat dissipation mesh 6 can be cleaned.

[0049] The router body 1 has a slot 4 on one side that is adapted to the L-shaped fixing block 31. The inner walls of the U-shaped groove 5 have sliding grooves 9 on both sides. Multiple sliders 8 are slidably connected inside the two sliding grooves 9. The multiple sliders 8 are respectively connected to the heat dissipation mesh 6 and the two sides of the fixing plate 7.

[0050] The working principle of the AI ​​marketing cross-system automatic invocation device based on the MCP protocol provided by this invention is as follows: When the components inside the router body 1 are fixed, first remove the threaded sleeves 33 on the surface of the two external threaded blocks 32. After the two threaded sleeves 33 are separated from the two external threaded blocks 32, pull the two L-shaped fixing blocks 31 to separate the disassembly base plate 2 from the router body 1, and the components inside the router body 1 can be exposed for inspection and repair.

[0051] Compared with related technologies, the AI ​​marketing cross-system automatic invocation device based on the MCP protocol provided by this invention has the following beneficial effects: This invention provides an AI marketing cross-system automatic invocation device based on the MCP protocol. A disassembly base plate 2 and two fixing components 3 are set at the bottom of the router body 1 to facilitate disassembly when the internal components of the router body 1 fail. A U-shaped groove 5, a heat dissipation mesh 6 and a fixing plate 7 are set on the surface of the disassembly base plate 2 to dissipate the heat generated when the router body 1 is working.

[0052] The above description is merely an embodiment of the present invention and does not limit the patent scope of the present invention. Any equivalent structural or procedural transformations made based on the content of the present invention specification and drawings, or direct or indirect applications in other related technical fields, are similarly included within the patent protection scope of the present invention.

Claims

1. A method for automatic cross-system invocation of AI marketing based on the MCP protocol, characterized in that, Includes the following steps: S1. Receive marketing task instructions generated by the AI ​​marketing system; S2. Parse the marketing task instructions, extract task elements, and label them with scene tags; S3. Based on the scene tags and task types, match available tools from the MCP registration tool library; S4. Perform permission verification and security checks; S5. By invoking the execution engine, the available tools are invoked in accordance with the standardized format of the MCP protocol; S6. Collect the execution results of each tool and generate an execution report in a unified format; S7. Feed the execution report back to the AI ​​marketing system.

2. The AI ​​marketing cross-system automatic invocation method based on the MCP protocol according to claim 1, characterized in that, The task elements in S2 include at least one of the following: target audience, marketing channels, triggering conditions, and priority. The scenario tags include at least one of the following: hotel, pharmacy, business travel, retail, and education.

3. The AI ​​marketing cross-system automatic invocation method based on the MCP protocol according to claim 1, characterized in that, The call execution engine in S5 supports at least one of the following call modes: synchronous call, asynchronous call, and streaming call, and has built-in retry mechanism, timeout control, and error handling functions.

4. The AI ​​marketing cross-system automatic invocation method based on the MCP protocol according to claim 1, characterized in that, The S3 algorithm, based on semantic similarity calculation and weight matching, dynamically selects the optimal tool combination and supports hot-swapping and dynamic expansion of tools.

5. The AI ​​marketing cross-system automatic invocation method based on the MCP protocol according to claim 1, characterized in that, The unified format execution report in S6 includes at least one of the following: structured data, unstructured content, execution status, error information, reach rate, and conversion rate prediction.

6. An AI marketing cross-system automatic invocation device based on the MCP protocol, characterized in that, include: The module includes a task parsing module, a tool library index module, a call execution engine, and a result feedback module. The task parsing module is used to receive AI marketing task instructions, extract task elements and label scene tags, and generate standardized MCP call requests. The tool library index module is used to maintain the MCP registration tool metadata database and supports intelligent tool matching based on scene tags and task types; The call execution engine is used to execute standardized calls to the MCP protocol and supports multiple call modes. The results feedback module is used to collect the execution results of each tool, generate execution reports in a unified format, and feed them back to the AI ​​marketing system.

7. The AI ​​marketing cross-system automatic invocation device based on the MCP protocol according to claim 6, characterized in that, The task parsing module has a built-in marketing task parsing standard, which defines a standardized task description format that includes target audience, marketing channels, triggering conditions, and priority. The tool library index module supports dynamic discovery and version management of tool capabilities and maintains the mapping relationship between scene tags and tool capabilities.

8. The AI ​​marketing cross-system automatic invocation device based on the MCP protocol according to claim 6, characterized in that, The execution engine includes a parallel call optimization unit, which distributes parallelizable subtasks to multiple tools for execution simultaneously through task decomposition and dependency analysis. The result feedback module includes a data cleaning unit, a format conversion unit, and an effect analysis unit, which perform multi-dimensional processing and analysis on the execution results.

9. The AI ​​marketing cross-system automatic invocation device based on the MCP protocol according to claim 6, characterized in that, The tool library index module uses a network device, which includes a router body, a disassembly base plate, two fixing components, a U-shaped groove, a heat dissipation mesh, and a fixing plate. The disassembly base plate is located at the bottom of the router body. The two fixing components are respectively located between the disassembly base plate and the router body. Each fixing component includes an L-shaped fixing block, an external threaded block, and a threaded sleeve. The L-shaped fixing block is connected to the end of the disassembly base plate, the external threaded block is connected to the side of the router body, and the threaded sleeve is threaded to the surface of the external threaded block. The U-shaped groove is formed on the surface of the disassembly base plate, the heat dissipation mesh is located inside the U-shaped groove, and the fixing plate is located inside the U-shaped groove and on one side of the heat dissipation mesh.

10. The AI ​​marketing cross-system automatic invocation device based on the MCP protocol according to claim 9, characterized in that, The router body has a slot on one side that fits the L-shaped fixing block. The inner walls of the U-shaped slot have sliding grooves on both the left and right sides. Multiple sliders are slidably connected inside the two sliding grooves. The multiple sliders are respectively connected to the heat dissipation mesh and the two sides of the fixing plate.

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